Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 © Scholarlink Research Institute Journals, 2014 (ISSN: 2141-7024) jetems.scholarlinkresearch.org Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016)

The Effect of Lax Environmental Laws on Foreign Direct Investment Inflows in Developing Countries Hoda Hassaballa The Department of Economics, The American University in Cairo, Egypt. AUC New Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835. _______________________________________________________________________________________ Abstract This paper investigates the effect of lax environmental laws on foreign direct investment (FDI) inflows in developing countries. Lax environmental laws in host countries may attract polluting FDI that usually escapes from stringent environmental laws in home countries. This is examined in a dynamic panel data model. For that, a fixed effect panel data model with homogenous slope is used. Besides the traditional determinants of FDI inflows, the effect of human capital and environment on FDI is tested. The empirical results indicate that a) lax environmental laws are a statistically significant determinant of FDI inflows. b) Lax environmental laws are the most influential determinants of FDI inflows in developing countries. c) Human capital is not a statistically significant determinant of FDI inflows in developing countries. In addition to these results, policy implications for developing countries are given. This is very essential in order to solve the ongoing dilemma of how to promote FDI inflows without leading to environmental degradation. __________________________________________________________________________________________ Keywords: foreign direct investment, lax environmental laws, developing countries, dynamic panel data model. (1) shows the increase in FDI inflows from 19952009.

INTRODUCTION For a long period of time FDI was seen as the rescuer of developing countries from low rates of growth through its effect on filling the saving gap, increasing managerial abilities and decreasing foreign exchange shortages (Aliyu, 2005). Accordingly, increasing trade liberalization and free movement of capital will stimulate growth. Yet, trade liberalization and FDI are becoming a real environmental threat. In particular, FDI may reduce welfare through increasing polluting emissions level and resource depletion. This happens when polluting FDI are concentrated in developing countries as a result of lax environmental laws. According to the pollution haven hypothesis, there is a positive relationship between FDI inflows and loose environmental laws. This is because the freer the trade and movement of capital is, the greater the shift of polluting industries from countries with stringent environmental laws to countries with loose environmental laws will be. Aliyu (2005) showed that there are three dimensions for this hypothesis. The first dimension is that based on the comparative advantage theory, developing countries may impose loose environmental laws to attract FDI and hence specialize in polluting industries. The second dimension is that as a result of stringent environmental laws, developed countries will damp their polluting wastes through FDI in developing countries. The third dimension is the immense depletion of developing countries resources such as petroleum, forests and timber by giant corporations. These effects are magnified when considering the rising trend of FDI inflows. Figure

The scale of FDI has increased rapidly during the period 1980- 2000. As reported by Fredriksson (2003), nominal FDI inflows worldwide increased by 18 % per year during 1987-1997. This result is also assured by the figures of the Organization for Economic Co-operation and Development (OECD) of both FDI inflows and outflows for OECD countries. However, FDI

Source: Calculated using data from UNCTAD World Investment Report, 2010. Figure (1): FDI Inflows by Region (1995-2009) US$ Billions 305

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) composition and the relative importance of its determinants have changed across time (Noorbakhsh et al, 2001). For instance, FDI was mainly in the primary sector, and natural resources were the most influential determinant of FDI in the 50’s (McKern, 1996; UNCTAD, 1998). The picture has changed since the 60’s as FDI was more directed to the industrial sector with a decline in natural resource importance. Escaping trade barriers can be a possible explanation for changes in FDI flows. Furthermore, market size and economic growth have become more attractive to foreign investors (UNCTAD, 1998). However, since the 80’s FDI inflows were directed to services and technology based manufacturing. For example, FDI inflows in the services sector constituted 60% of FDI inflows in 1990 (UNCTAD, 1993). The growing importance for the services sector is also true for the case of FDI inflows in developing countries. However, the petroleum sector, construction, chemicals production and transportation are still major recipients of FDI inflows in developing countries (UNCTAD, 2012). Hence, polluting industries are still directed to developing countries via FDI. Table (1) shows FDI inflows to developing countries by sector.

developing countries among the world largest 20 recipients of FDI inflows in 2012.

Table 1: FDI Inflows to Developing Countries by Sector (US$ Billions) Period 1989-1991 2005-2007

Primary 3.9 46.8

Manufacturing 16.1 121.0

In billions of dollars, (x) = 2011 ranking. Source: UNACTD, 2013 Figure (2): Top 20 FDI host economies in 2012

Services 9.3 161.4

Source: Calculated using data from UNCTAD, World Investment Report, 2009.

Among developing countries, China has been the major recipient of FDI inflows since 1992. Furthermore, China is the second largest recipient in the world after the US. In 2012, East and South East Asia constituted 46.4% of FDI flows to developing countries. Latin America and the Caribbean came second with 34.7% of FDI flows to developing countries. On the other hand, Africa received only 7.1% of FDI inflows to developing countries (UNCTAD, 2013). More details are found in figure (3) which illustrates FDI inflows to developing countries in 2012.

Examining the recent trends of FDI inflows in developing countries by region and by sector highlights the importance of studying the determinants of FDI inflows in these economies. For that, the objective of this paper is to investigate the effect of lax environmental laws and human capital on FDI inflows in developing countries. This is examined in a dynamic panel data model for 22 developing countries over the period 1990-2010. Accordingly, a fixed effect panel data model with homogenous slopes is used. The rest of the paper is organized as follows: Section 2 describes the flow of FDI by location to developing countries; section 3 provides a quick theoretical background with empirical evidences; section 4 discusses the empirical model; finally, conclusion and policy implications are presented in section 5.

The growth figures of global FDI slowed down recently from $1652 billion in 2011 to $1351 billion in 2012 (UNCTAD, 2013). Weak economic growth, deficiencies in stock markets and institutional obstacles are among the causes that may lead to this slow down (Mihci et al., 2005). In addition, the Arab Spring since 2011 has led to political and economic instability which negatively affected FDI inflows to the Middle East. North Africa was the major recipient of FDI inflows to Africa. In 2011, FDI inflows to the continent decreased by 50% to reach $7.69 billion with an insignificant flows to Egypt and Libya (UNCTAD, 2012).

THE FLOW OF FDI TO DEVELOPING COUNTRIES In 2012, the share of FDI inflows to developing countries was 52% of the world FDI inflows surpassing flows to developed countries by $142 billion for the first time (UNCTAD, 2013). Not only this, but as shown in figure (2) there were 9 306

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) macroeconomic stability, trade openness, market size, labor cost and energy availability. Table (2) summarizes the empirical evidences with respect to the effect of the traditional determinants on FDI inflows. Recently, the effects of human capital and the environment on FDI inflows are considered. The economic literature addressed the importance of human capital as a determinant of FDI inflows. For instance, Lucas (1990) deduced that lack of human capital decreases FDI inflows. In addition, Dunning (1988) showed that labor skills and education affect the size of FDI inflows and the type of production by TNCs. Furthermore, Zhang and Markusen (1999) formulated a model that showed that human capital affects both the volume and the destination of FDI inflows. Source: Calculated from data given by UNCTAD World Investment Report, 2013. Figure (3): FDI Inflows in Developing Countries in 2012

Table 2: Determinants of FDI – Summary of the literature Determinants of FDI Openness

THEORATICAL BACKGROUND AND EMPIRICAL EVIDENCES To understand the nature of the relationship of FDI and the environment from a theoretical point of view, it is appropriate first to discuss briefly the theory of FDI. The classical explanation of FDI depends mainly on capital arbitrage phenomenon. Differences in marginal rate of returns is what causes FDI; that is, capital moves from low rates of return countries to relatively higher marginal rates of return ones. Carius (2002, p.4) showed that economic and political conditions are what affect FDI location. Political conditions include such factors as political stability, environmental laws and administrative capacities. On the other hand, economic conditions include factors such as GDP growth rate, policies governing trade, macroeconomic stability, infrastructure, cost and types of production. The market-size hypothesis states for instance that FDI will not be initiated unless certain size of the market is reached. This market size is essential to achieve economic efficiency (Mihci et al., 2005).

Infrastructure quality

Positive Edwards (1990) Gastanaga et al (1998),Hausmann & FernadezArias(2000) Wheeler & Mody (1992),Kumar (1994), Loree& Guisinger (1995)

Schnelder & Frey (1985), Tsai (1994) & Lipsey (1999)

Edwards (1990) Jasperson, Aylward, & Knox (2000)

Labour cost

Wheeler & Mody (1992)

Schnelder & Frey (1985)

Political instability

Insignificant

Tsai (1994), Loree& Guisinger (1995) & Lipsey (1999)

Real GDP per capita

Taxes and tariffs

Besides political and economic conditions, there are other factors that affect FDI. Among these factors, there are the promotions offered by developing countries to attract FDI. Tax incentives, exemption from strict environmental laws, or any other regulations imposed on domestic firms are examples of these promotions. Finally, the decision of FDI can be very much affected by trade barriers conditions as it can be more profitable sometimes to be engaged in FDI rather than trade as suggested by the tariff jumping hypothesis (Mihci, et al., 2005).

Negative

Loree& Guisinger (1995), Gastanaga et al (1998)& Wei (2000) Schnelder & Frey (1985) & Edwards (1990)

Loree& Guisinger (1995),,Wei (2000) Hausmann & FernandezArias (2000) Tsai (1994), Loree& Guisinger (1995) & Lipsey (1999) Wheeler & Mody (1992) & Lipsey(1999)

Loree& Guisinger (1995), Jasperson, Aylward, & Knox (2000) & FernandezArias (2000)

Source: Asiedu (2002) On the empirical level, the effect of human capital on FDI inflows is a debatable issue. For instance, Root

Accordingly, FDI inflows are affected by many factors. The classical or traditional factors include 307

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) developed countries. Likewise, there is no strong evidence to support the hypothesis that loose environmental laws form an attraction to FDI inflows. In addition, Copeland and Taylor (2003) stated that strict environmental laws do not affect the direction of FDI but rather it is the type of instruments used. They also argued that there can be a pollution haven effect and not a hypothesis.

and Ahmed (1979) and Schneider and Frey (1985) showed that human capital is not a significant determinant of FDI inflows. On the other hand, Hanson (1996) showed that human capital affects FDI inflows but not as political stability and security of property rights. Noorbakhsh et al (2001) also concluded from their research effort that human capital is not only a significant determinant of FDI inflows, but also it is one of the most influential factors and its importance is increasing across time.

Another remarkable finding was reached by the OECD (1997) which discovered that most polluting industries in developed countries are directed via FDI to other developed and not developing countries. In addition, the polluting FDI inflows directed to developing countries represented a lesser proportion of total FDI receipts in 1992 than in 1972.

Concerning environment as a determinant of FDI inflows, the classical trade perspective of comparative advantage considers environment as another factor of production where stringent environmental laws increase production costs. Accordingly, developed countries with stringent environmental laws will have relatively high production costs. Consequently, these countries will not have comparative advantage in polluting industries as they cannot compete. While developing countries with loose environmental laws will have comparative advantage in polluting industries due to relatively lower production costs. Hence, developing countries will specialize in polluting industries and their lax environmental laws will attract polluting FDI.

On the other hand, Kolstad and Xing (1998) conducted an empirical analysis to test the effect of stringency of the environmental laws in destination countries on the location of dirty industries. They discovered that US FDI chemical industry outflows and the stringency of environmental laws of the foreign destination country exhibit a statistically significant negative linear relationship. Nevertheless, this relationship is not really apparent for less polluting FDI industries. The evidence is stronger that loose environmental laws form a source of attraction to polluting FDI flows. This result was also achieved by Co et al. (2004) in their study of US FDI outflows to developed and developing countries. They studied two manufacturing industries in a panel data model from 1982-1992. Their results confirmed that the stringency of environmental laws influences investment decisions as there is an inverse relationship between environmental standards and FDI flows for the average developing countries despite of some possible exceptions for this finding. Furthermore, Smarzynska and Wei (2001) found out that there could be a support for the pollution haven hypothesis if the country’s participation in international environmental agreements was taken as a measure for the environmental standards of a country. In their research, 543 major multinational corporations in 24 countries in Central/Eastern Europe and the former Soviet Republics were studied using firm level data instead of the country/industry level data on investment.

According to the pollution haven hypothesis, there is a positive relationship between FDI inflows and loose environmental laws. This is because the freer the trade and movement of capital is, the greater the shift of polluting industries from countries with stringent environmental laws to countries with loose environmental laws will become. Two empirical results could be deduced from this hypothesis. First, there is a positive relationship between lax environmental laws and FDI inflows in developing countries. Also, there is a positive relationship between FDI outflows and stringency of environmental laws in developed countries. Opposite to this, the neo-technology trade perspective states that FDI and the environment exhibit a positive relationship. For example, the pollution halo hypothesis believes that FDI inflows can have a positive effect on the environment. This is through the transfer of environmental friendly techniques of production via FDI from developed countries to developing countries that rely on environmental damaging techniques (Doytch, 2012).

In addition, Mihci et al. (2005) formed a model that was founded on the integrated approach of Dunning (1981; 1988) to study the FDI-environment relationship. They constructed several equations to examine the effects of various factors on FDI inflows and outflows. Many samples were used to examine the consistency of the independent variables such as FDI between developed and developing OECD countries, FDI in bilateral agreements between all

On the empirical level, FDI-environment relationship is indistinct. For example, Levinson (1996) provides an empirical literature survey on how sensitive FDI to environmental regulations in US is at the international and domestic levels. He discovered that after more than twenty years of empirical research, the evidence is weak to support that stringent environmental laws push polluting FDI away from 308

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) OECD countries and total inflows to OECD countries. The most remarkable finding was the effect of the environmental variable on FDI in most of the samples. The index of environmental sensitivity performance in the reporter country (IESP) was used as a proxy of strict environmental laws. The main result was that IESP in the reporter country and FDI outflows had a positive significant relationship. Lastly, Aliyu (2005) constructed an econometric model for the period of 1990-2000 to study the effect of environmental laws on FDI outflows in 11 developed OECD countries. His study also evaluated the effects of FDI inflows on pollution emissions in 14 non OECD developing countries. The results showed that there is a positive correlation between FDI outflows of polluting industries and the stringency of environmental policies in developed countries.

becomes an important issue in the case of presence of many explanatory variables. The Choice of Variables Many proxies have been used to assess the stringency or laxity of environmental laws in previous studies. These include: the degree of participation in international environment treaties (Smarzynska and Wei, 2001), the index of environmental sensitivity performance IESP (Mihci et al., 2005), carbon dioxide emissions (Hoffmann et al., 2005) and environmental tax (Aliyu, 2005). Environmental tax is a more reliable proxy for the stringency of environmental laws if compared to other proxies. This is because participating in international environmental treaties does not necessarily mean that they are enforced in practice. In addition, data for IESP is available for very short time series. Still, due to the availability of data, carbon dioxide emissions are the chosen proxy variable for lax environmental laws in this research. It could be argued, then, that high carbon dioxide emissions are a reflection of lax environmental laws which could be a source of attraction for FDI. Accordingly, the relationship between FDI inflows and carbon dioxide emissions is expected to be positive.

Following this review of empirical studies on FDIenvironment relationship, it could be deduced that this area of research is controversial and a hot debatable issue in the field of economics. Hence, the research problem is the FDI-environment controversy. Further empirical research is needed to have a clearer picture. For that, this research work forms another step forward to have a better comprehension of the nature of FDI-environment relationship.

Higher level education is the most influential element of human capital (OECD, 1998; World Bank, 1998). Thus, correct policies with respect to higher education are a precondition for improving the skills of human capital. This in return will give the host country a locational advantage (Noorbakhsh et al, 2001). For that, secondary school enrollment ratio is used in this study to measure human capital. This variable was used before by many researchers such as Root and Ahmed (1979), Schneider and Frey (1985) and Noorbakhsh et al (2001). It is expected then to have a positive relationship between FDI inflows and secondary school enrollment ratio.

THE EMPIRICAL MODEL The empirical model investigates the effect of lax environmental laws on FDI inflows in developing countries. This research uses a dynamic panel model that is founded on the econometric work of Noorbakhsh et al (2001). For this purpose, the model uses a panel data set for developing countries over the period 1990-2010 for which data is available. The list of countries is shown in appendix A.1. Missing data were calculated through the use of linear interpolation.

In order to guarantee high profits and efficiency, certain size of the host country market must be reached as suggested by the market size hypothesis. Many studies such as that of Root and Ahmed (1979), Schneider and Frey (1985) and UNCTC (1992) showed that the market size in the host country is an influential determinant of FDI inflow. Theoretically speaking, the bigger the market and the higher the rate of growth of GDP are, the more investment opportunities are available. Therefore, the rate of growth of GDP is used to reflect the market size. This was used before in many studies such as Lim (1983), Singh & Jun (1995) and Torrisi (1985). Following this logic, it is expected to have a positive relationship between FDI inflows and the rate of growth of GDP.

The Econometric Approach Consider the following fixed effect panel data model with homogenous slopes:

FDIit = α Envit + β ' X it + λi + ε it

(1)

For a country i at time t, FDI is the net FDI inflows as a percentage of GDP; Env is the measure of lax environmental laws; X is a vector of explanatory variables which includes determinants of FDI inflows other than environment; λ is the fixed effect dummy variable for individual unobserved effects and ε is the error term. Using panel data enlarges the sample size for better estimation and improves the power of the test statistics. Furthermore, panel data is preferred because it increases degrees of freedom. This

Labor cost is another determinant of FDI inflows especially in labor intensive industries. However 309

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) factors include economic conditions, availability or even technological abilities.

when tested empirically, there are evidences with and others against the aforementioned hypothesis. For instance, Flamm (1984) and Lucas (1993) showed that wage cost variable is a significant determinant of FDI inflows. Others like Kravis and Lipsey (1982) showed that this result is not always true. A possible explanation for this discrepancy in results is that sometimes if high wages are used as proxy for skills, FDI inflows may be attracted to destinations of high wages. There are many measures for labor cost such as the wage differential as used by Noorbakhsh et al (2001) or the growth rate of the labor force used by Dunning (1973). Due to data availability, this paper uses the growth rate of the labor force. The logic here is that the more available the labor is, the lower his cost and the more FDI inflows will be.

factors

Lagged change in FDI inflows is included to test whether any persistence or correction is happening with respect to FDI. Foreign direct investors usually carry on calculation measures regarding continuity of their investment based on conditions in host and home countries. Previous FDI inflows contain information regarding the conditions in host countries (Noorbakhsh et al, 2001). In addition, Johanson and Wiedersheim Paul (1993) showed that investors prefer investing in countries that they were introduced to them before. It is worth mentioning that other variables such as democracy, risk and natural resource availability can be used as explanatory variables. Nevertheless, the sample period is 1990-2010 which marks the decline in the importance of natural resource availability for FDI inflows (UNCTAD, 1998). Also, studies by Loree and Guisinger (1995), Jasperson, Aylward, and Knox (2000), as well as Hausmann and FernandezArias (2000) showed that political instability has insignificant effect on FDI inflows. In addition, studies like Noorbakhsh et al (2001) showed that risk variable is insignificant. Agarwal (1980) referred that to the variation of different guarantees for political risk in home countries. Also, the variables used to reflect democracy or risk are based on subjective assessment and may not reflect properly the measured variable (Noorbakhsh et al, 2001). For that, these variables are not included in the model.

Trade openness is another important determinant of FDI inflows. The logical reason behind this is that the more liberalized the trade is, the more confidence the investors will have in this market. Following previous studies as UNCTAD (1999) and Noorbakhsh et al (2001), trade openness is measured by the ratio of the sum of exports and imports to GDP. The sign of the trade openness coefficient is expected to be positive. Macroeconomic stability is another source of attraction to FDI inflows. Financial liberalization is used as a proxy for macroeconomic stability by many researchers such as Root and Ahmed (1979) and Noorbakhsh et al (2001). The logic behind this choice as suggested by Fry (1997), and Easterly and Schimidt-Hebbel (1993) is that besides financial liberalization’s attraction of FDI inflows, it is a necessary condition for macroeconomic stability. This is due to the fact that the increase in private sector share of domestic credit is usually coupled with inflation stabilization. For that, the share of domestic credit to the private sector in GDP is used as a proxy for macroeconomic stability in this study.

Estimation To examine the effect of lax environmental laws on FDI inflows in developing countries, a dynamic panel data model is used. The sample studied is for 22 developing countries over the period 1990-2010. Hausman test between fixed effect and random effect models was conducted. Accordingly, Random effect model was rejected. Hence, a fixed effect panel data model with homogenous slopes was used as shown in equation 1. Table (3) shows the results of Panel least squares estimation of equation 1 where at time t for country i, FDI inflows is regressed on lagged change of FDI (∆FDI-1), carbon dioxide emissions (CO2), labor availability (AVL), market size (MS), trade openness (TO), macroeconomic stability (MAC), secondary school enrollment ratio (SSE), net energy imports (ENG) and a trend term.

Cheap energy source is another vital determinant of FDI inflows. According to the UNCTAD (1998), the availability of energy sources that are accessible is one of the biggest concerns of foreign investors. Following Noorbakhsh et al (2001), net energy imports is the variable chosen to reflect energy availability. Net energy imports can be defined as the difference between energy use and energy production as a percentage of energy use. The coefficient of the net energy imports is expected to be negative since it is a measure of shortages of energy.

Most explanatory variables including carbon dioxide emissions were significant with the exception of labor availability and market size. All of them got the expected signs with the exception of labor availability and human capital. However, testing for heteroskedasticity through white test indicated its presence. Accordingly, White correction was implemented to achieve heteroskedasticity-consistent

In addition to these explanatory variables, a trend term and lagged change in FDI inflows are also considered. A trend term is used to reflect unobserved components such as promotion efforts, decrease in corruption, administration efficiency or even a combined supply side factors that reflect conditions in home countries. These supply side 310

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) estimation. Table (4) shows the results of the estimation after White correction.

coefficient of carbon dioxide is positive and significant at the 5% level. The coefficients of macroeconomic stability and the trend term are positive and significant at 5% level as well. In addition, the positive significant coefficient of the lagged change in FDI at 1 % level indicates that there is persistence in FDI inflows. This shows that investors prefer investing in markets they were introduced to before. The coefficient of energy is significant and negative at 10% level. This highlights the importance of cheap energy sources as a precondition for FDI inflows. The coefficients of human capital, trade openness and market size are all insignificant.

Table 3: Empirical Results of Equation 1a Dependent variable FDI ∆FDI -1 CO2 AVL MS TO MAC SSE ENG Trend Constant Adjusted R Squared a

Coefficients 0.257478 (4.814655)* 1.113600 (2.368246)** -0.051762 (-0.832071) 0.047227 (1.700945) 0.021207 (2.106012)** 0.040133 (3.856280)* -0.032202 (-2.265641)** -0.016140 (-3.505768)* 0.126640 (4.400143)* -2.371303 (-1.868084) 0.502500

As a robust check, the model has been re-estimated after applying general least squares (GLS) weights estimation as an alternative to White correction for heteroskedasticity. Table (5) shows the results of this re-estimation. Table 5: Empirical Results of Equation 1 a (General Least Squares Weights) Dependent variable FDI ∆FDI -1

t- values are in parentheses * Significance at 1% level ** Significance at 5% level

CO2 AVL

Table 4: Empirical Results of Equation 1 a (White Correction) Dependent variable FDI ∆FDI -1 CO2 AVL MS TO MAC SSE ENG Trend Constant Adjusted R Squared

MS

Coefficients

TO

0.257478 (4,147305)* 1.113600 (2.254594)** -0.051762 (-0.422550) 0.047227 (1.215299) 0.021207 (1.098492) 0.040133 (2.453759)** -0.032202 (-1.067181) -0.016140 (-1.956948)*** 0.126640 (2.049607)** -2.371303 (-1.950125)*** 0.502500

MAC SSE ENG Trend Constant Adjusted R Squared

Coefficients 0.244528 (5.099925)* 1.031815 (3.608723)* 0.017934 (0.472639) 0.035865 (1.920831)*** 0.018788 (2.801079)* 0.032742 (4.645176)* 0.005929 (0.637127) -0.010082 (-3.404768)* 0.044899 (2.267673)** -3.171310 (-3.969344)* 0.634665

a

t- values are in parentheses * Significance at 1% level ** Significance at 5% level *** Significance at 10% level After applying general least squares weights, the empirical results indicate the following: Most explanatory variables are found significant and with the correct signs with few exceptions. Among these exceptions are the insignificant coefficients of labor availability and human capital. In addition, the energy coefficient is significant and negative similar to the white corrected estimation. Again, this indicates that the more the shortages of energy are, the less FDI inflows will be. The coefficient of carbon dioxide is significant at 1% level and positive.

a

t- values are in parentheses * Significance at 1% level ** Significance at 5% level *** Significance at 10% level After adopting white correction, the empirical results of the model show that lax environmental laws are a significant determinant of FDI inflows. The 311

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) correcting for heteroskedasticity, after adopting White correction and for general least squares weights estimation. The empirical results in all cases indicated that lax environmental laws are a statistically significant determinant of FDI inflows and that it is one of the most influential determinants of FDI inflows in developing countries. This finding in particular represents the contribution of this paper as it emphasizes the effect of the environment on FDI inflows.

This indicates that lax environmental laws attract FDI inflows in developing countries. Also, the coefficients of macroeconomic stability and trade openness are significant and positive at 1% level. This indicates that economic stability and the extent of free trade increase foreign investors’ confidence in host countries’ markets. In addition, the coefficient of market size is positive and significant at 10% level. This moves in line with the market size hypothesis. With respect to the trend term, it was found positive and significant at 5% level. Finally, there is persistence in FDI inflows as suggested by the positive significant lagged change in FDI coefficient.

Surprisingly enough, the coefficient of human capital was only significant in the first regression. This contradicts with the results of Noorbakhsh et al.( 2001). Accordingly, the effect of human capital on FDI inflows is still a debatable issue. A possible explanation though for insignificant human capital effect is the lack of policies needed to develop human capital. Poor education and lack of skilled labor are common features in many developing countries.

One can deduce then that lax environmental laws attract FDI inflows. It would be interesting as well to determine its relative importance in attracting FDI inflows. This is more clarified in table (6). Examining the relative importance of each of the explanatory variables shows that lax environmental laws are the most influential determinant of FDI inflows in developing countries. This is because it has the largest estimated beta coefficients of 0.20986 and 0.1024 for White corrected estimators and weighted GLS respectively. Second in importance is the change in lagged FDI inflows, followed by the trend term, macroeconomic stability and market size, trade openness and finally energy shortages.

Most other traditional explanatory variables were found significant and with the correct signs with the exception of labor availability. With respect to labor availability, it was found insignificant in all the three estimations. This reflects the lack of an effective policy related to labor. The energy coefficient was significant and negative. This shows that cheap energy sources are attract FDI. Macroeconomic stability and lagged change in FDI were always significant and positive in the three regressions which highlight the importance of economic stability and the persisting nature of FDI inflows. Finally, trade openness and market size were among the significant determinants of FDI after adopting general least squares weights estimation. In addition, the trend term was always positive and significant in the three regressions.

Table 6: Relative Importance of Explanatory Variables in Attracting FDI Inflows (Estimated Beta Coefficients)* Dependent Variable: FDI ∆FDI -1

White Correction

GLS Weights

0.0061

0.004

CO2

0.20986

0.1024

AVL

-0.0024

0.000236

MS

0.0007

0.000232

TO

0.000156

0.000044

MAC

0.00025

0.00008

SSE

-0.00037

0.00002

ENG

-0.00005

-0.00001

Trend

0.00298

0.0003

Since the empirical results showed that lax environmental laws attract FDI inflows in developing countries, certain policies have to be put in action. This has to be tackled with delicacy. This is because if this process continues, we will end up with a depletion of developing countries’ resources, increase in pollution level and environmental degradation of developing countries. However, if this process stops at once, developing countries will lose huge volume of FDI inflows which is a cornerstone for their development. Hence, to take the maximum benefit of this with the least cost, the following policy implications are suggested: 1. It is very essential for developing countries to get rid of corruption. 2. Spread firm’s and public awareness about the importance of preserving the environment. 3. Be updated with the new production techniques and ensure their accessibility. 4. Ensure transparency and information availability.

* The estimated beta coefficients (units free) are equal to the product of the estimated coefficient of the explanatory variable and the ratio of standard deviation of the explanatory variable to that of the dependent variable. CONCLUSION & POLICY IMPLICATIONS This paper examined the effect of lax environmental laws and human capital on FDI inflows in developing countries in a dynamic panel data model. Accordingly, besides the traditional determinants of FDI inflows, carbon dioxide emissions and secondary school enrollment ratios were included in the regression. A fixed effect panel data model was used. This was carried for three regressions: Before 312

Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) Aliyu, Mohammed Aminu (2005). Foreign direct investment and the environment: pollution havens hypothesis revisited. Annual Conference on Global Economic Analysis, Germany.

5. Adopt stringent environmental laws and ensure their enforcement and compliances. This can be done for instance by adopting a policy mix between command and control approach and economic incentive approach. These regulatory approaches are explained briefly in appendix A.2. 6. Make sure that FDI and the environment are complementing each other and get rid of their conventional treatment as substitutes. 7. Make the maximum benefit of other influential determinants of FDI inflows such as macroeconomic stability, cheap energy sources or the persisting nature of FDI. This is to form a source of attraction for FDI inflows that will substitute the role played by lax environmental laws. 8. Offer more incentive, promotions, free zones and tax breaks that encourage FDI inflows. 9. Adopt the correct policies with respect to labor in general and human capital in particular to form another source of attraction for FDI. This can be done through better education, spreading knowledge, research and development, offering training programs to labor to improve their skills,…etc. 10. Encourage public private partnership (PPP) to encourage FDI.

Asiedu, E. (2002).On the determinants of foreign direct investment to developing countries: is Africa different? World Development, 30, 107-119. Carius, A. (2002). Environmental impacts of foreign direct investment in the mining sector in the newly independent states, Conference Paper, OECD Global Forum on: International Investment: Conference on Foreign Direct Investment and the Environment. “Lessons to be learned from the Mining Sector” February 7-8, Paris, France Co, Catherine Y., List, J.A. and Qui, L.D. (2004). Intellectual property rights, environmental regulations and FDI. Land Economics 80, 153-173. Copeland, Brain R. and Taylor, M.S. (2003). Trade, growth & the environment, National Cambridge: Bureau of Economic Research working paper, No.9823. Doytch, N. (2012). FDI halo vs. pollution haven hypothesis. 65th Annual Meeting NYSEA Proceedings, Volume 5.

All in all, there is a strong relationship between FDI and the environment. This was confirmed by the results of this research work. Putting this in mind enables us to have a clearer picture on how to tackle the FDI-environment dilemma in order to promote FDI inflows and at the same time preserve the environment in developing countries. The suggested policy implications open the door for a possible path to tackle this matter. However, a lot is still needed to ensure their applicability in practice. For that, I suggest conducting research on FDI-environment relationship on a country level basis. This is because the main limitation of this study is the unavailability of data for some of the countries which made it difficult to use a fixed effect panel data with heterogeneous slopes. Using heterogeneous slopes would have allowed suggesting policy implications for individual countries. Hence, collecting reliable data on macro and micro levels should be among the major concerns of governments and research centers of developing countries. If so, it is more advisable to study this relationship on an industrial level as well.

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ACKNOWLEDGEMENT The author would like to express her deep gratitude to Dr. Fatma El Diwany for her valuable advice.

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Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 5(3):305-315 (ISSN: 2141-7016) financially rewarded for reducing pollution further below the legal levels. Finally, non mandatory approach relies on voluntary actions of firms to upgrade their environmental performance. Environmental self regulation by firms assumes that self interest is the reason behind firms’ adoption of voluntary action to reduce pollution. For example, firms are afraid of losing public acceptance as a reaction to the disclosure of environmental information about the firms.

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