FDI and Environmental Pollution in Taiwanese Manufacturing Industry Abstract This paper investigates the relationship between environmental pollution and foreign direct investment (FDI) from a firm level data of Taiwanese manufacturing industry. Our work combines the outward FDI and the annual manufacturing survey, a total of 2,172 firm-level observations that covers the years between 2000 and 2004 is included in the empirical estimation. By using a multinomial logit model, we analyze whether the Taiwanese manufacturing firms would transfer the pollution through the outward investment. Our results show that the more the firms environmental protection expenditures in the home country, the more the outward investments. The findings are consistent with pollution haven effects. Moreover, by the terms of labor cost as well as home and host country s environmental standards, we find that Taiwanese manufacturing firms intend to transfer the pollution activities through the outward investment. The behavior of Taiwanese outward investment indeed supports the pollution haven hypothesis. JEL Code:F23; Q2 Keywords:foreign direct investment ( FDI), pollution haven effect (PHE), pollution haven hypothesis (PHH)
1. Introduction Among the debates of the effects of globalization, the relationships between economic growth, environmental pollution, and foreign direct investment (FDI) had recently obtained a great of extensive discussions. 1 Opponents of globalization often worry about the adverse effects of trade on environmental quality, and argue that free trade would cause the movements of pollution industry from one country to another. The debate started in the early 1990s, while the critics focus on the issue that multinationals located in rich and tightly regulated countries, such as US and Canada, would flock to a poor and laxly regulated country (e.g., Mexico) under the North-American Free Trade Agreement (NAFTA). This is the so-called pollution haven hypothesis (PHH), which the economist states that the industries with pollution intense would move to the country with laxly environmental standards through the FDI. The existing literatures on the PHH can be divided into three aspects. One line of research attempts to explain the differences on the decision of location choices within the US by foreign investors. Such as, List and Co (2000) use a variety of stringency measurements and conclude that heterogeneous environmental policies across states do matter to foreign multinational firm s site choice. Keller and Levinson (2002) and Fredriksson et al. (2003) find that abatement costs have a moderate deterrent effect on foreign investment into US states. A second line of research tries to examine the relationship between FDI from industrialized to developing countries and the stringency of environmental regulations. Most of these studies seldom show that a positive and significant correlation between industry pollution costs and the FDI activities from developed country exists (Dean 1992; Zarsky 1999; Febry and Zenghi 2000; smarzynska and Wei 2004). A third line of research seeks to investigate the relationship between trade flow and environmental standard, while these studies also find that there is no consistency between the empirical results and the PHH (Eskeland and Harrison 2003; Hoffmann et al. 2004; Waldkirch and Gopinath 2004; Dean et al. 2005). 2 Despite the possibility of moving polluting activities to developing countries has been the issue of the globalization debate and there is a rapidly growing area in this 1 Many of the literature focus on the relationship between income growth and environmental pollution. Grossman and Krueger (1995) propose the relationship under an inverted u-curve that has found supports in many studies (see Selden and Song, 1994). That is, pollution will first increase with income, then decrease at higher income levels. The initial upward relationship occurs because of a positive relationship between output and emissions. The downward tendency occurs when higher demand for environmental quality at higher income levels forces the introduction of cleaner technologies (the technique effect) and an output combination which is less polluting (the composition effect). 2 For reviews of the empirical literature, see appendix A. 1
field of the research, however, the answer is not settled. Eskeland and Harrison (2003) pointed out a possible reason is that the existing literatures are primarily based on case studies, and Fredriksson et al. (2003) find that factors of bureaucratic corruption destroy the effect of environmental policy on the FDI. Taylor (2004) further pointed out that empirical work on the PHH has sometimes confused two quite different concepts. The first concept is a Pollution Haven Effect (PHE) which relates changes in environmental policy to resulting changes in trade flows. second concept is the Pollution Haven Hypothesis (PHH) which predicts that when trade barriers are reduced, pollution-intensive industries will transfer from countries with stringent environmental regulation to countries with lax environmental regulation. The Most researches have confirmed the existence of PHE by examining the relationship between changes of environmental regulations among the industries and varieties of trade flows, however, these studies have not verified the PHH further. 3 This paper aims to analyze the outward investment behavior of Taiwanese manufacturing firms. Based on a set of firm level data over the period of 2000-2004, this study concentrates mainly on the relationship between FDI and environmental pollution. and PHH. Apart from the previous empirical studies, first, we examine both of PHE Secondly, we adopt the multinomial logit model to catch the decision of the individual firm s outward investment behavior. activities transfer or not could be confirmed. Then, whether the pollution The remainders of this paper are organized as follows. In the next section we describe the dataset used for the empirical analysis and address the empirical specification. In section 3, empirical results are presented with a detailed discussion. Conclusion is given in the final section. 2. Data Source, Empirical Specification and Variables Description 2.1 Data Source The data utilized in this study are taken from several sources including annual surveys on outward foreign direct investment and manufacturing operations conducted by the Ministry of Economic Affairs (MOEA), Taiwan, datasets constructed by World Bank, International Monetary Fund, and World Economic Forum. According to the statistics on outward investment reported by Investment Commission in MOEA, the average amounts of the approved outward investments had rapidly increased by the rate of 41% per year in the period from 1991 to 2001. The figure shown in 2000 was 7.7 billions and has reached the peak over the period. 3 Previous studies usually create the index of environmental pollution by the emission of pollutant or the pollution abatement cost. 2
Therefore, we choose the firm-level data in manufacturing from the year 2000. The data of firm-level outward investment are collected by the Survey on Outward Foreign Direct Investment from 2001 to 2004, which includes information on the amounts of outward investment, the invested locations, and the invested industries. The characteristics data of the individual firm including the numbers of employees, the revenues of sales, the amounts of fixed costs and R&D are collected from Annual Survey on Manufacturing-Plant Operations. The environmental variables, e.g. the emission of carbon dioxide and the emission of organic water, are gathered by the dataset of World Bank. The last, the variables of the characteristics of the country, like the amount of total production in host country and the degree of corruption, adopts from the International Financial Statistics and the Global Competitiveness Report in World Economic Forum. To make sure the reasonability of the data, small firms with having the employees less than 30 and the revenues of sale less than 10 millions are omitted. There is a total of 2,172 observations included in the estimation. the average value of investment in the polluted and non-polluted industries. Table 2-1 shows We find that the average amounts of the outward investment in the polluted industries are slightly lower than those in the non-polluted industries. However, those firms that invest in the polluted industries outwardly have significantly higher environmental expenditures domestically than those invest in the non-polluted industries. 2.2 Empirical Specification and Variables Description The empirical analysis in this paper is divided into two parts: First, following the previous empirical works on the relationship between environmental protection expenditures and trade flows, we construct a model to test the pollution haven effect. Secondly, we further analyze whether Taiwanese manufacturing firms has shown a behavior of transferring the pollution through outward investment. This provides us the judgment to verify the pollution haven hypothesis. 2.2.1 Test of Pollution Haven Effect To investigate the relationship of pollution and foreign direct investment empirically, the model specifications are established in equation (1): FDI i SIZE KL RDS 0 PINDU 6 1 i i i GDP CPI 7 2 k 3 8 i k 4LABORi 5ENVi. (1) i 3
The dependent variable FDIi denotes the logarithm of the stocks of foreign direct investment for firm i. The independent variables consist of the characteristics of the firm and the characteristics of the host country. Referring to previous empirical studies on determinants of FDI, the firm s characteristics include firm size ( SIZE ), capital intensity ( KL ), R&D intensity ( RDS ) and wage payment ( LABOR ). SIZE is measured by sales in logarithmic term, KL is measured by the ratio of the net value of fixed assets to total employment, and RDS is measured as the R&D expenditure to sales ratio. The term LABOR is measured by the logarithm of the number of employees per firm. proxy of each firm s wage payment. The term ENV It is used as a is denoted as the logarithm of environmental protection expenditure for each firm. The term PINDU is a dummy variable which represents whether the industry of investments abroad is belong to pollution industry, and take the value of 1 if the forward investment is in the pollution industry. 4 In terms of host country s characteristics, we add the host country s GDP to control the economic state of each host country. Following the suggestions of Fredriksson et al. (2003), the corruption is also included in the FDI regression to explain the effects of corruption on FDI. We measure the degree ofa host country s corruption ( CPI ) by the corruption perceptions index which collected from the Global Competitiveness Report. number implies lower possibility of corruption. By the original source, it describes that a higher The parameter of particular interest in this equation is 5, and a positive 5 implies that a stronger environmental protection in the home country tends to encourage outward FDI. represented by 5 >0. In other words, the polution haven efect can be 2.2.2 Test of Pollution Haven Hypothesis In order to examine the pollution haven hypothesis directly, we further adopted the Multinomial Logit (MNL) Model to test whether Taiwanese manufacturing firms have shown a behavior of transferring the pollution through outward investment. 5 4 According to the definition proposed by World Bank, an industry included in pulp, paper and paper products, chemical material, chemical products, petroleum and coal product, non-metallic mineral products and basic metal industries, are classified as pollution industries. 5 See Greene (2003), Chapter 21 for a more fully specification of the multinomial logit model. 4
According to the industrial code of the firm in the home country and the outward investment choice for each firm to infer whether the firms have the behaviors of transferring pollution activities, we create the dependent variables by these rules. The state of outward investments can be divided into three types: First, the selected industry of the outward investment is in the non-pollution industries, and this indicates that the behavior of transferring pollution activities is not exist (TP =0). Second, the firm is in a non-pollution industry in home country, but the selected industry of the outward investment belongs to the pollution industry. This represents a weakly transferring pollution activity (TP =1). Third, the firm is in a pollution industry in home country, and the selected industry of the outward investment is also in the pollution industry. This reveals that the behavior of strongly transferring pollution activities (TP =2). Therefore, the vectors of relative probability in the degree of pollution in multinomial logit model are constructed in equation (2): TP ij ln j X i j 1, 2. (2) TP i0 The dependent variable TPij represents the investment status of outward investment firm i (j=0, non-transfer; j=1, weakly transfer; j=2, strongly transfer the pollution activity). The independent variables consist of investment firm s characteristics, the measurement of pollution intensity, the relationship between Taiwan and host country, and the indexes of host country s environmental standards. In the first part of the independent variables, the investment firm s characteristics include firm size ( SIZE ), which is measured by sales in logarithmic term, and R&D intensity ( RDS ), which is measured as the R&D expenditure to sales ratio. In the second part, we use the logarithm of expenditures of environmental protection to capture the pollution intensity for each investment firm ( ENV ). 6 It is important to note that the more value of this variable, the more the firms environmental protection expenditures in the home country. Therefore, there are more incentives to transfer pollution from Taiwan to abroad for Taiwanese manufacturing firms. In the third part, we use the wage differentials between host country and Taiwan within two-digit manufacturing industry ( DWAGE ) to capture the relationship between Taiwan and host country. 7 This variable reflects the labor cost and indicates that the more the wage differentials, the more the production costs in home country 6 Eskaland and Harrison (2003) and Smarzynska and Wei (2004) also create the pollution intensity variable by this measurement. 7 Wage data are from the Ministry of Economic Affairs, Taiwan and International Labor Organization. 5
for each firm. So, there are more incentives to invest countries with lower labor costs. Finaly, the indexes of host country s environmental standard include the emission of carbon dioxide ( CO 2 ) and organic water ( PWATER ). The more the value of both variables, the more the pollution levels in the host country. Therefore, there are more incentives to transfer pollution activities to the country with laxly environmental standard for Taiwanese manufacturing firms. Table 2-2 reports the summary statistics for the variables used in the regressions 3. Estimation Results 3.1 Empirical Results for PHE We begin the test of PHE by estimating equation (1) using WLS which accounts for the heteroskedasticity. If the sign of the coefficient for ENV is positive, it represents that firms in the home country with higher environmental protection expenditures are paid higher production costs than those in the host country with lower environmental standard. Table 3-1 displays the results of WLS estimators. The first model only takes firm s characteristics as explanatory variables and it is specified as a basic model. The second model adds more variables including firm s characteristics and host country s characteristics. Concerning the effects of firm s characteristics, Table 3-1 shows the signs of the parameter estimates for variables of firm s characteristics are generally consistent with most previous works. The estimated coefficient of SIZE is positive and significant at the 1% level, this is because the firm with large size often combines the oligopoly market structure. Thus, they can gain benefits in host country by comparative advantages (Horst, 1972). The sign of KL is also positive and significant at the 1% level, indicating that the firm with more capital-labor ratio tends to increase activities of outward investment. In addition, the variable LABOR exhibits a significant and positive effect at the 1% level. It indicates that the outward investment firms seek the other countries with cheaper production factor for reducing production costs, and this is consistent with the suggestion of Kojima (1973). One point worth noting is that the impact of expenditures of environmental protection on the stocks of foreign direct investment. The sign of ENV is positive and significant at the 1% level. Clearly, the positive relationship between expenditures of environmental protection and the stocks of foreign direct investment suggests that PHE holds for Taiwanese manufacturing firms. However, we could not 6
verify the PHH on this stage, but the following works will focus on the important issue. As theoretical literature has mentioned, corruption affects foreign direct investment through its impact on environmental policy stringency. Thus, we add the variable of the degree of corruption in host country. The sign of CPI is negative and significant, indicating that the FDI decreases as the value of CPI increase. it means the more the level of corruption of the host country, the more incentives for Taiwanese manufacturing firms. Moreover, we find that the coefficient of ENV becomes smaller in model (2), indicating that ENV keeps a strong effect on firm s FDI, while has a 3% lower impact as we take into account the host country s corruption problem. 3.2 Empirical Results for PHH As shown in previous section, we have confirmed the existence of PHE for Taiwanese manufacturing firms. We further analyze whether the outward investment of Taiwanese manufacturing firms have a behavior of transferring in pollution industry. The dependent variable which indicates investment status of outward investment by firm i can be divided into three types: non-transfer, weakly transfer and strongly transfer the pollution activities. pollution activity and it is specified as the base group. The first type is denoted as a non-transfer The results of multinomial logit model, presented in terms of marginal effects, can be found in Table 3-2. The differences between model (3) and mode (4) are the measurement of environmental standard for host country, and they are the emission of carbon dioxide ( CO 2 ) and organic water ( PWATER ). Interesting results emerge when we separate firms into three groups according to firm s outward investment choices with non-transfer, weakly transfer, and strongly transfer the pollution activities. At first, we look into the test on PHH. As shown in the strongly transfer group of Table 3-2, the coefficients of ENV model (4) are positive and statistically significant at 1%. in model (3) and The economic implication is that the more the environmental protection expenditures of the firm in the home country, the more incentives in outward investment to the country with laxly environmental standard, and the main reason of the incentives is to avoid the environmental protection responsibility. In terms of the relationship between Taiwan and host country, we use the wage 7
differentials to capture the impact of labor costs on FDI. 8 One interesting and important finding we found is that the coefficient of DWAGE in model (3) and model (4) are positive and statistically significant at 1% for weakly and strongly transfer group. That is, the countries with lower labor costs are really the conditions of attracting investments for Taiwanese manufacturing firms. Moreover, concerning the effect ofthe indexes of host country s environmental standard, the coefficients for CO2 are positive and statistically significant at 1% for weakly and strongly transfer group, whilst the sign of PWATER is positive and statistically significant at 10% for weakly transfer group. In terms of labor costs, home and host country s environmental standards, the pattern of Taiwanese outward investment indeed presents that transferring the behavior of pollution industries exists. pollution haven hypothesis. So, our empirical results support the 4. Conclusion As the world s population grows, and trade expands, future trade and environment conflicts will arise. Although there have been much interests in analyzing the relationship between FDI and pollution to provide the evidence result for PHH, few attempts have been made to examine both PHE and PHH. This paper aims to analyze the outward investment behavior of firm in Taiwanese manufacturing industry. Based on a set of firm level data which we combine the abroad investment survey with the annual manufacturing-plant survey. Using multinomial logit model, we analyze whether the Taiwanese manufacturing with pollution intense transfer through the outward investment. The major findings in this study are summarized as below. First, the firms environmental protection expenditures in the home country indeed have a significantly positive impact on firm s outward investments for Taiwanese manufacturing. It means that the more the firms environmental protection expenditures in the home country, the more the outward investments. This result is consistent with pollution haven effects. Moreover, we further investigate whether the Taiwanese manufacturing industry with pollution intense transfer through the outward investment. In terms of labor costs, home and host country s environmental standards, the pattern of Taiwanese outward investment indeed presents that transferring the behavior of pollution industries exists. Our empirical results support 8 We have also tried to control the degree of corruption in the host country. However, the correlation between CPI and DWAGE is too high. Thus the variable CPI is dropped in Equation (2). 8
the pollution haven hypothesis. 9
Appendix A:Empirical literature review Study Data Results Kolstad and Xing (1998) Country level data 1985~1990 They report a positive association between the amount of sulfur emissions in a host country and inflows of U.S. FDI in heavily polluting industries. Support PHH. List and Co (2000) U.S. state level data 1986~1993 The relationship between site choice and state environmental regulations is explored, using four measures of regulatory stringency. They find evidence that heterogeneous environment policies across states do matter. Keller and Levinson U.S. state level data After control unobserved heterogeneity with fixed state or regional effects, environmental costs (2002) Eskeland and Harrison (2003) Fredriksson et al. (2003) Hoffmann et al. (2004) Smarzynska and Wei (2004) Waldkirch and Gopinath (2004) Cole and Elliott (2005) Dean et al. (2005) Cole et al (2006) 1977~1994 Firm level data Côte d'ivoire 1977~1987 Venezuela 1983~1988 Morocco 1985~1990 Mexico 1990 U.S. state level data 1977~1987 Country level data Firm level data 1989~1994 Industry level data 1994~2000 Industry level data 1989~1994 Firm level data 1993~1996 Country level data 1982~1992 have a statistically significant deterrent effect on FDI. They find no evidence that foreign investment in these developing countries is related to abatement costs in industrialized countries. However, they find some evidence that foreign investors are concentrated in sectors with high levels of air pollution. Mixed results. They suggest environmental policy and corruption both play a significant role in determining the spatial allocation of inbound US FDI. In low income countries, CO 2 -levels Granger cause inward FDI flows. Support PHH. They find no support for the pollution haven hypothesis, and no systematic evidence that FDI form dirtier industries is more likely to go to countries with weak environmental regulations. They find a positive correlation between FDI and pollution that is both statistically and economically significant in the case of the highly regulated sulfur dioxide emissions. They find the level of pollution abatement costs in a US industry to be a statistically significant determinant of that industry s FDI providing evidence of a pollution haven effect. They find evidence of haven seeking behavior, but only in highly polluting industries by investors form Hong Kong, Macao, and Taiwan. And they find on significant evidence of such behavior by OECD investors. FDI is found to affect environmental policy, and the effect is conditional on the local government s degree of corruptibility. If the degree of corruptibility is sufficiently high, FDI leads to less stringent environmental policy, and FDI thus contributes to the creation of a pollution haven.
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Table 2-1 The status of outward investment invest in pollution industry invest in non-pollution industry Average No. of obs. Average FDI flow (1000NT$) environmental protection expenditure (1000NT$) 365 164,582.3 10,242.1 1807 178,477.9 2,508.3 13
Table 2-2 Variable Definition and Basic Statistics Variables Definition Mean (S.D.) TP FDI SIZE KL RDS LABOR ENV PINDU DWAGE CO2 PWATER GDP CPI the investment status for outward investment firm i (whether transfer the pollution industry) FDI flow(1000nt$) sales(1000nt$) net value of fixed assets to total employment ratio (%) R&D expenditure to sales ratio (%) the number of employees environmental protection expenditure(1000nt$) whether outward invest in pollution industry wage differential between Taiwan and host country ( per month/ us dollar) the emission of organic water in the host country (per capita/ per ton) the emission of carbon dioxide in the host country (per capita/ per day/ per kilogram) logarithm of the host country s GDP host country s corruption 0.2274 (0.6099) 176,142 (1,139,426) 1,718,035 (7,082,869) 2341.02 (3395.29) 0.0255 (0.0123) 257 (646) 3,807.9 (34,714.4) 0.168 (0.374) 835.42 (740.39) 4.2392 (5.4611) 0.1391 (0.0094) 27.72 (1.24) 4.07 (1.61) 14
Table 3-1 Empirical results for PHE:Weight least regression Indep. Var. Model (1) Model (2) Coef. S.D. Coef. S.D. SIZE 0.2454*** 0.06 0.2763*** 0.0624 KL 0.1209*** 0.0291 0.0576*** 0.0247 RDS -0.6029 0.3723-0.5352* 0.3164 LABOR 0.4*** 0.0891 0.3542*** 0.0898 ENV 0.0585*** 0.0152 0.0562*** 0.0153 PINDU -0.0502 0.1355-0.0006 0.1359 GDP 0.0017 0.0401 CPI -0.5318*** 0.1577 Cons 3.7552*** 0.5284 4.8812*** 1.144 ***, ** and * denote coefficient significant at 1%, 5% and 10% statistical level, respectively. 15
Table 3-2 Empirical results for PHH:Multinomial logistic regression SIZE RDS KL ENV DWAGE CO2 PWATER Log Weakly (TP=1) -0.79E-04 (0.0025) -0.2684** (0.1315) 0.45E-03 (0.0018) 0.12E-03 (0.96E-03) 0.54E-04*** (0.1E-04) 0.0055*** (0.0018) -867.1106 Model (3) Model (4) Strongly (TP=2) -0.31E-02 (0.0042) -0.1521 (0.1573) 0.0135*** (0.0038) 0.011*** (0.0016) 0.14E-03*** (0.02E-03) 0.0165*** (0.0029) Weakly (TP=1) 0.97E-03 (0.0025) -0.2292* (0.1342) 0.8E-03 (0.0019) -0.16E-03 (0.98E-03) 0.23E-04*** (0.1E-04) 0.0093* (0.0048) -885.9518 Strongly (TP=2) 0.11E-03 (0.0045) -0.0862 (0.1551) 0.0158*** (0.0041) 0.0113*** (0.0017) 0.27E-04*** (0.1E-04) -0.005 (0.0105) likelihood ***, ** and * denote coefficient significant at 1%, 5% and 10% statistical level, respectively. 16