Review of Economics and Development Studies Vol. 5, No 4, 2019 741 Volume and Issues Obtainable at Center for Sustainability Research and Consultancy Review of Economics and Development Studies ISSN:2519-9692 ISSN (E): 2519-9706 Volume 5: No. 4, 2019 Journal homepage: www.publishing.globalcsrc.org/reads Is Pakistan Really a Pollution Haven Country? 1 Hina, 2 Javed Iqbal, 3 Asia Baig, 4 Muhammad Yusuf Amin 1 MS Scholar, Department of Economics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan: hinakhan5617@yahoo.com 2 Assistant Professor, Department of Economics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan. 3 Demonstrator, Department of Economics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan. 4 Lecturer, Institute of Business Studies and Leadership, Abdul Wali Khan University Mardan, KPK, Pakistan ARTICLE DETAILS ABSTRACT History Revised format: 30 Nov 2019 Available Online: 31 Dec 2019 In this study, the Pollution Haven Hypothesis (PHH) validity has been tested for Pakistan and its impact on exports of Pakistan has been investigated as well. The PHH predicts that environmental regulations‟ variability among countries affects polluting industries location and trade flows. Autoregressive Distributed Lag (ARDL) or bound test of cointegration is used to investigate the short and long-run relationships. We found positive and statistically significant short-run and long-run relationships between CO2 (proxy for lax environmental policy) and FDI inflows. Finally, Trade Balance Index (TBI) of metal and mining, primary iron & steel, chemicals and rubber products does not support the PHH. While the pulp & paper and the textile industry validated the existence of pollution haven effect. We can conclude that PHH does exist for Pakistan and therefore such policies are needed that encourage FDI inflows that do not adversely affect the environment. © 2019 The authors, under a Creative Commons Attribution-Non Commercial 4.0 Keywords Pollution Haven Hypothesis, Foreign Direct Investment, Environmental Regulations, Autoregressive Distributed Lag, Trade Balance Index JEL Classification: P45, K32, K39 Corresponding author‟s email address: hinakhan5617@yahoo.com Recommended citation: Hina, Iqbal, J., Baig, A. and Amin, M., (2019). Is Pakistan Really a Pollution Haven Country? Review of Economics and Development Studies, 5 (4), 741-754 DOI: 10.26710/reads.v5i4.885 1. Introduction Inflows of FDI have increased almost in every region of the world but major trend is observed towards the developing countries (Ullah, Shah, & Khan, 2014). FDI inflows to Asian region increased to $541 billion in 2016 due to significant growth in the East and South Asian Economies (World Investment Report, 2016). FDI is like a doubled-edged sword for economies as if it enhances economic growth, labor productivity and innovation but also harms environment (Wang, Gu, David, & Yim, 2013). Many developing countries are already facing severe water scarcity, poor air quality and floods. The unchecked FDI inflows may further worsen environmental conditions (Gamso, 2018; Cole & Elliott, 2005). Conversely, some studies argue that an increasing level of FDI inflows reduce the air pollution by adopting more advance technology (Kirkulak, Qiu, & Yin, 2011). Air quality in Pakistan is deteriorating due to increase in CO2 emissions. The highest temperature http://www.publishing.globalcsrc.org/reads Review of Economics and Development Studies Vol. 5, No 4, 2019 742 recorded in Pakistan reached to 53.5 0 C in 2017 causing many deaths (Ellis, Saifi & Martinez, 2015; Wasif, 2017). Annual cost of environmental degradation has increased to Rs. 450 billion (Ali, waqas & Ahmad, 2015). The gravity of the problem can be gauged from pattern of CO2 emissions and FDI in figure 1. The graph shows that CO2 emissions in Pakistan have been showing an increasing trend over a period of time. Pakistan' emission per unit of output (emission intensity) is almost double of the average world intensity (Qureshi, 2006). Figure 1: FDI and CO2 Emissions in Pakistan (Compiled from the World Bank, 2017). In Pakistan, major investment has taken place in the polluted sectors of oil & gas, textile, construction, power, chemicals & transport (Board of Investment, 2018). A $33 billion investment in energy projects under the China Pakistan Economic Corridor (CPEC) of which some is based on coal is an environmental concern too. While exports are pre-requisite for economic growth, environment quality is also important for good quality of life. The larger FDI inflows coupled with the existing environmental deterioration in developing countries raise questions of pollution havens. To answer this question, this study empirically investigates the FDI-environment and trade-environment relations in case of Pakistan. 2. Literature Review The nexuses between FDI and economic growth is explained through either endogenous growth theories (Romer, 1986) or the Environmental Kuznets Curve (EKC). Further related to FDI flows there are two prominent theories, the one is the Porter hypothesis and another is the pollution haven hypothesis (Baek & Koo, 2009). The empirical work on PHH has been generally with mixed results. Birdsall and Wheeler (1993) found contradictory results to PHH for Latin America. Levinson (1996) found that if state differences in environmental regulations are large, then pollution haven effect can be observed, however, if differences between states are very small then the pollution haven effect fades. However, Eskeland and Harrison (2003) reported that foreign firms are less polluted compared to domestic plants. Cole and Elliott (2005) used data of 59 developed and developing countries on FDI and found no evidence for PHH. Fan (2012) studied the FDI-investment decision in Chinese provinces based on enviornmental stringency and found no evidence that investor favor lax enviornmental enforcmemt. Keho (2015) used CO2 emission as a proxy for measuring environmental pollution and no long run effect of FDI on country's environment was found. Contrary to the above, Aliyu (2005), Hoffmann, Lee, Ramasamy, and Yeung (2005), Beak and Koo (2008), Ridzune, Avazalipour, Zandi, Saberi, Hakimipour, and Damankeshideh (2013), Noor and Ahmad (2014), and Aliyu and Ismail (2015) reported the existance of the PHH. that pollution haven hypothesis existed. 0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.2 0.4 0.6 0.8 1 1.2 Year CO2 FDI Review of Economics and Development Studies Vol. 5, No 4, 2019 743 3. Research Methods Grossman and Krueger (1991) pioneered the work on PHH explaining the trade flows and environmental quality between the United States and Mexico. The Heckscher-Ohlin (H-O) and the factor endowment theories also provide a framework for trade-environment nexus and that how environmental regulations affect trade patterns and multinational investment decisions. These theories predict that country will specialize in production and exporting the products that uses the locally abundant factor. Also, they envisage environment to be treated as a productive resource and its assimilative and regenerative capacities as natural endowments. Therefore, jurisdictions with stricter environmental regulations may inflict additional cost on pollution-intensive sectors. However, such firms may relocate to the less stringent regions to gain comparative advantage (Ratnayake & Wydveled, 1998). Following the Kolstad (2011), we present the PHH as follows: (1) where is economic activity and could be exports, FDI or employment while corresponds to environmental regulations, is a matrix of other control variables and the last term is the error term. Based on Asghari (2012), Hassaballa (2014), Ratnayake and Wydeveld (1998), Sarmidi, Noor and Ridzuan (2015), Yoon and Heshmati (2017), and Zhang (2005) equation (1) can be represented as follows: (2) where is the FDI inflow in Pakistan in year t in sector i and represents the environmental regulation, is the vector of independent variables, while are the parameters to be estimated and is the random stochastic error term, and the subscript denotes country while is for time period. Equation (2) is further extended as follows. (3) The variables used in the estimation are summarized in the table 1 as follows: TABLE 1: VARIABLES DESCRIPTION Variable Description Foreign direct investment (% age of GDP) Carbon dioxide emissions (metric ton per capita) used as proxy of pollution level; High levels of CO2 represent laxity of environmental standards. According to PHH, relationship between FDI and CO2 must be positive. Gross domestic product (% age annual growth rate) measuring the wellbeing of the country and market size. Based on previous literature and theories expected relationship between FDI and GDPGR is positive. Labor force participation (% of total population) and expected relationship between FDI and Lab is positive Gross secondary school enrollment used as a proxy for skilled labor Gross capital formation (% age of GDP) Review of Economics and Development Studies Vol. 5, No 4, 2019 744 Energy import (% age of energy use) to reflect the energy availability and expected sign for this variable is negative Trade openness (export plus import as a proportion of GDP), more liberalize the trade is the more confidence the investor will have in this market. Data on the relevant variables have been collected from the World Development Indicators (WDI) for 1980-2017 period. Covariates have been selected on the basses of Dunning „eclectic‟ theory which states that FDI is attracted due to the reasons such as (a) location specific advantages which include availability of raw material at low cost, (b) advantages due exclusive ownership of certain assets such as technology, patents, trademarks and skills and (c) internalization advantage which protects them against the market failures. Lax environmental regulations offer location specific advantage (Ratnayake & Wydeveld, 1998). Trade Balance Index (TBI) can be used to test the export competitiveness in polluting industries. A country specializes in export (as net-exporter) or import (as net-importer) in dirty products for group of products based on the TBI of Lafay (1992). This study used products that are classified as per the Standard International Trade Classification (SITC) specifically the 3-digit SITC Revision 2. TBI index is formulated as follows: where stands for exports and imports of country for agroup of goods . The TBI takes value from minus one to one. A negative value indicates that the country is “net-importer” and a positive one as “net-exporter”. 4. Results and Discussions Dickey & Fuller (1979, 1981) and Phillips & Perron (1988) tests are used to examine the stationarity of the time series data. Following the Engle and Granger (1987), Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) tests both with and without trend are provided in Table 2. The unit root test demonstrates that the labor force participation rate and gross domestic product growth rate are stationary at level while rest are stationary at first difference. Table 2: ADF and PP Stationarity Tests Variables ADF methodology PP methodology W it h o u t T re n d P ro b a b il it y W it h T re n d P ro b a b il it y W it h o u t T re n d P ro b a b il it y W it h T re n d P ro b a b il it y FDI (%age of GDP) -2.80 0.06 -3.05 0.13 -1.86 0.34 -1.85 0.66 CO2 emission (MT/PC) -1.65 0.44 -4.62 0.00 -0.74 0.82 -2.68 0.25 GDP (Annual Growth rate) -3.89 0.00 -3.81 0.02 -3.85 0.00 -3.81 0.02 LAB (%age total population) -4.78 0.00 -5.47 0.00 -4.80 0.00 -5.47 0.00 SKL (Gross Percentage) -0.71 0.83 -2.90 0.17 -0.38 0.90 -2.86 0.18 CAP (%age of GDP) -1.68 0.43 -2.62 0.27 -1.76 0.39 -2.69 0.24 ENG (%age of energy used) -2.01 0.28 -2.87 0.18 -2.04 0.26 -2.33 0.41 Trade Openness (%age of GDP) -1.71 0.41 -2.44 0.43 -1.77 0.38 -2.56 0.30 FDI (%age of GDP) -4.03 0.00 -3.98 0.01 -4.00 0.00 -3.95 0.02 CO2 emission (MT/PC) -5.97 0.00 -4.11 0.01 -6.66 0.00 -6.48 0.00 Review of Economics and Development Studies Vol. 5, No 4, 2019 745 GDP (Annual Growth Rate) -7.67 0.00 -7.70 0.00 -10.73 0.00 -16.57 0.00 LAB (%age total population) -9.87 0.00 -9.82 0.00 -19.99 0.00 -33.58 0.00 SKL (Gross Percentage) -6.32 0.00 -5.22 0.00 -9.52 0.00 -9.55 0.00 CAP (%age of GDP) -6.07 0.00 -5.98 0.00 -6.07 0.00 -5.98 0.00 ENG (%age of energy used) -5.17 0.00 -5.12 0.00 -5.16 0.00 -5.10 0.00 Trade Openness (%age of GDP) -7.73 0.00 -7.78 0.00 -7.75 0.00 -7.84 0.00 Note: indicating unit root at first difference. ARDL bounds test developed by Pesaran, Shin & Smith, (1996) and Pesaran et al., (2001) does not require same order of integration for all the variables. This approach is even valid for I(0), or I(1) or even if some are I(0) and some are I(1). ARDL model based on equation (3) can be specified as follows: ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ where represents first difference operator, is a drift element, is a white noise error term. The terms with summation show error correction for short-run estimates, while rest of the terms shows the long-run relationship. The first step in ARDL method requires a selection of optimal lag length. A lag length one had the lowest Schwarz information criterion (SC) value and thus selected. The second step entails the estimation of ARDL model and then computing of F-statistics using ARDL bound test methodology. Both the calculated F-statistics and its critical values are provided in Table 3. The F- statistic (4.33) is greater than the upper bound critical value (4.26), and the null hypothesis of no long-run associations is rejected. Table 3: Testing the Long-Run Associations Model F-Statistic 4.33*** Critical value Bounds Significance Lower Bound Upper Bound 1% 2.96 4.26 5% 2.32 3.50 10% 2.03 3.13 *** INDICATE 1% SIGNIFICANCE LEVEL Table 4 reveals strong positive and statistically significant long run relationship between CO2 and FDI inflows. The effect of GDP growth on FDI inflow, as expected, is positive. Also, FDI inflow is positively affected by energy imported (ENG), gross capital formation (CAP), trade openness (OPEN) and labor force participation rate (LAB), although insignificantly. The secondary school enrollment ratio (SKL) affects FDI inflow significantly at five percent which is contrary to the general expectation. Table 4: ARDL Long-Run Estimation (Dependent Variable FDI) Regressor Coefficient Std. Error t-Statistic Prob. CO2 7.6589 1.8163 4.2168 0.0003*** GDPGR 0.3151 0.1541 2.0447 0.0511* Review of Economics and Development Studies Vol. 5, No 4, 2019 746 Lab 0.0089 0.0425 0.2115 0.8342 SKL -0.1064 0.0497 -2.1406 0.0419** CAP 0.1288 0.1263 1.0197 0.3173 ENG 0.1315 0.0785 1.6742 0.1061 OPEN 0.0911 0.0603 1.5097 0.1432 C -12.1214 4.1691 -2.9074 0.0074*** ***, **, * INDICATE 1%, 5% AND10% SIGNIFICANCE LEVELS Short-run error correction ARDL results are provided in table 5. In short-run there exists a positive and significant relation between CO2 and FDI inflows as well. Similarly, ENG positively affects FDI inflow. GDP growth does not affect FDI inflow significantly. Unlikely in the long run, secondary school enrollment ratio affects FDI inflow in short run. Likewise, in the long run, trade openness, labor force participation rate, gross capital formation affects FDI insignificantly with positive signs in the short run. Highly significant error correction term with negative sign indicates the presence of long-run relationship with 45 percent speed adjustment from previous year‟s disequilibrium in FDI inflow to current year‟s equilibrium. No evidence of serial correlation and hetroscadasticity is found. Table 5: Short-run ARDL Model (Dependent Variable Δ (FDI)) Cointegrating Form Variable Coefficient Std. Error t-Statistic Prob. Δ(CO2) 3.4659 0.9652 3.5907 0.0013*** Δ (GDPGR) 0.0591 0.0476 1.2416 0.2255 Δ (LAB) 0.0041 0.0193 0.2113 0.8343 Δ (SKL) 0.0230 0.0283 0.8155 0.4222 Δ (CAP) 0.0583 0.0613 0.9513 0.3502 Δ (ENG) 0.0595 0.0315 1.8907 0.0699* Δ (OPEN) 0.0412 0.0259 1.5867 0.1247 CointEq(-1) -0.4525 0.1089 -4.1545 0.0003*** R-squared 0.886025 Adjusted R-squared 0.842189 Breusch-Godfrey Serial Correlation LM Test: F-Statistic= 0.065662 Probability = 0.7999 Obs*R-squared= 0.96925 Probability = 0.7556 Heteroskedasticity Test: : Breusch-Pagan-Godfrey F-Statistic=1.519100 Probability =0.1885 Jerque Bera Normality Test Jerque Bera statistic = 4.1978 Probability = 0.1225 ***, **, * INDICATE 1% , 5% AND10% SIGNIFICANCE LEVELS The ARDL model stability is tested using CUSUM and CUSUM square tests. Figure 2 and figure 3 shows plots of the CUSUM and CUSUM squares indicating that all coefficients of the model are stable. Review of Economics and Development Studies Vol. 5, No 4, 2019 747 Figure: 2. Plot of CUSUM Figure: 3. Plot of CUSUMSQ Table 6 exhibits the outcomes of Granger pair wise causality analysis between the variables. We find that CO2 emissions granger causes FDI inflows and similarly the FDI inflows granger cause CO2 emissions (Bi-directional causality). The results can be interpreted that on one side lenient environmental regulations (higher levels of CO2 emissions) attracts the FDI while on another side FDI inflows are one of the major factors responsible for increasing level of pollutions. Results validate the unidirectional causality in case of GDPGR and FDI inflows running from GDPGR to FDI inflows. No casual evidence was found in case of labor force participation rate and foreign direct investment inflows. Skill labor granger causes FDI indicating that Pakistan's skill labor affects the foreign direct investment inflows. Causality from FDI inflows to gross capital formation exhibits that level of FDI affect the gross capital formation. Similarly, unidirectional causality was observed from energy imported to foreign direct investment inflows. Lastly, in case of openness and foreign direct investment no causality was found from either side. -15 -10 -5 0 5 10 15 92 94 96 98 00 02 04 06 08 10 12 14 16 CUSUM 5% Significance -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 92 94 96 98 00 02 04 06 08 10 12 14 16 CUSUM of Squares 5% Significance Review of Economics and Development Studies Vol. 5, No 4, 2019 748 Table 6: Pair wise Granger Causality Tests Granger Causality Results F-value Prob. Result 5.84697 3.23312 0.0020*** 0.0280** 2.30987 1.53678 0.0788* 0.2194 0.13613 0.32434 0.9821 0.8929 3.32343 0.61702 0.0219** 0.6881 0.83776 2.69508 0.5372 0.0479** 2.14192 0.11800 0.0982* 0.9870 0.55852 0.54038 0.7305 0.7436 ***, **, * INDICATE 1%, 5% AND10% SIGNIFICANCE LEVELS Sector wise per capita emission data is not available for Pakistan and therefore aggregate level data were used. To study the prediction of pollution haven hypothesis that FDI contributes to developing countries exports due comparative advantage in polluting products. This study uses Pakistan trade statistics for most polluting industries to find if Pakistan exports increase due to its lax environmental regulations. Table 7 shows the seven most polluting sectors of Pakistan along with the product codes. Furthermore, figure 4 shows calculated TBI values for the said products. Table 8 reports the unit root results of TBI. The TBI index of metal and mining, primary iron and steel, chemicals and rubber products does not support the pollution haven hypothesis because the value of TBI is between 0 and -1 which shows that Pakistan imports more as compare exports. While the pulp and paper industry provide evidence for the pollution haven effect as the TBI value reached to 1 in 1994 showing the country became net exporter and thereafter TBI value have been continuously increasing showing an increase in exports. Pulp & paper industry TBI has unit root so pollution haven does exist. The TBI values of textile industry from 1982 till 1996 shows Pakistan as net exporter of textile products with varying from 1997 to 2017. TBI values for textile industry has unit root which also validate the existence of pollution haven effect. Table 7: Selected Pollution intensive industries of Pakistan Industry Product Code Metal & Mining 281, 282, 287, 288, 289 Primary Nonferrous Metals 681, 682, 683, 684, 685,686, 687,689 Pulp & Paper 251, 641, 642 Primary Iron & Steel 671, 672, 673, 674, 675, 676, 677, 678,679 Chemicals 512, 513, 514, 582, 583, 584, 585 Textile 261, 262, 263, 264, 265, 266, 267, 268, 269 Rubber Products 621, 625, 628 Source: (Qureshi, 2006; Indriya & Widodo, 2011) Review of Economics and Development Studies Vol. 5, No 4, 2019 749 Figure 4: Pollution Intensive Industries Trade Performance, TBI values Table 8: Unit Root Results of TBI Unit Root Test of TBI Industry t- values (Prob.*) Decision Metal and Mining -4.4* (0.0013) Stationary (does not support PHH) Primary Nonferrous Metal -5.92* (0.0000) Stationary (does not support PHH) Pulp and Paper -2.30 (0.1777) Has unit root (Confirm PHH) Primary Iron and Steel -6.3* ( 0.000) Stationary (does not support PHH) Chemicals -3.7* (0.0084) Stationary (does not support PHH) Textile -0.58 (0.8612) Has unit root (Confirm PHH) Rubber Products -6.16* (0.0000) Stationary (does not support PHH) 5. Discussions and Conclusions This study investigated the pollution haven hypothesis which predicts that environmental regulations variability among countries or regions affect polluting industries location and trade flows. A positive and statistically significant relationship between CO2 emissions and FDI inflows both in short-run, and long- run was found. Therefore, we can conclude that lenient environmental policy of Pakistan may attract polluting industries from other countries. Similar conclusions are drawn by Aliyu (2005), Acharyya (2009), Baghebo & Apere (2014), Hoffmann, et al. (2005), Hassaballa, (2014), Leslie (2016) and Ridzune, Noor & Ahmad (2014). We found weak evidence of pollution havens from exports side except for the textile, paper and pulp industries of Pakistan similar to the results of Akbostanci, Tunc, & Turut- Asik (2007), Indriya & Widodo (2011), and Qureshi, (2006). Pakistani government needs to invest in human capital and make their labour force more competitive and efficient to attract the foreign -1 0 1 1982 1987 1992 1997 2002 2007 2012 2017 Matel & Mining Primary Nonferrous Matels Pupl & Paper Iron & Steel Chemicals Textile Ruber Products Review of Economics and Development Studies Vol. 5, No 4, 2019 750 investment. References Aliyu, M. A. (2005, June). Foreign direct investment and the environment: Pollution haven hypothesis revisited. In Eight Annual Conference on Global Economic Analysis, Lübeck, Germany (pp. 9-11). AlIyua, A. J., & Ismail, N. W. (2015-b). Foreign Direct Investment and Pollution Haven: Does Energy Consumption Matter in African Countries? Journal of Economics and Management, 9 (S), 21 - 39. Akbostanci, E., Tunc, G. I., & TÜRÜT-AŞIK, S. (2007). Pollution haven hypothesis and the role of dirty industries in Turkey's exports. Environment and Development Economics, 12(2), 297-322. Asghari, M. (2012). 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