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DEGREE OF STRINGENCY MATTERS: REVISITING THE POLLUTION HAVEN HYPOTHESIS BASED ON HETEROGENEOUS PANELS AND AGGREGATE DATA

Published online by Cambridge University Press:  20 February 2018

Thomas Jobert ,
Fatih Karanfil  and
Anna Tykhonenko
Thomas Jobert
Affiliation:
GREDEG, Nice Sophia Antipolis University
Fatih Karanfil*
Affiliation:
EconomiX, University of Paris Nanterre, King Abdullah Petroleum Studies and Research Center and Galatasaray University Economic Research Center
Anna Tykhonenko
Affiliation:
GREDEG, Nice Sophia Antipolis University
*
Address correspondence to: Fatih Karanfil, King Abdullah Petroleum Studies and Research Center (KAPSARC), P.O. Box 88550, Riyadh 11672, Saudi Arabia; emails: fatih.karanfil@kapsarc.org, fatih.karanfil@parisnanterre.fr.
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Abstract

Empirical studies on the trade-environment nexus that use panel data face two simultaneous challenges. One is associated with the potential presence of unobserved cross-country heterogeneity, while the other is due to the use of aggregate data. In this paper, we apply both the dynamic fixed effects and iterative empirical Bayes estimators to show first that when country heterogeneity is accurately accounted for in the estimation, it is possible to obtain significant impacts of trade variables on the environment, even though we use aggregate data. Second, using both the empirical Bayes parameter estimates and indicators of stringency of environmental regulations, we show that at low levels of stringency, the probability of having pollution-intensive foreign direct investments (FDIs) increases with a decrease in stringency. However, at high levels of regulatory stringency, more stringent regulations may lead to more pollution-intensive FDIs. This implies that pollution havens may exist only if environmental regulations are very lax or nonexistent.

Keywords

Foreign Direct InvestmentPollution HavenRegulatory StringencyBayesian Shrinkage Estimator

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Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 7 , October 2019 , pp. 2675 - 2697
Copyright
Copyright © Cambridge University Press 2018 

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References

REFERENCES

Albrecht, Johan (1998) Environmental policy and the inward investment position of US “dirty” industries. Intereconomics 33, 186–194.10.1007/BF02929512Google Scholar
Antweiler, Werner, Copeland, Brian, and Taylor, M. Scott (2001) Is free trade good for the environment? American Economic Review 91, 877–908.10.1257/aer.91.4.877Google Scholar
Baltagi, Badi H., Bresson, Georges, Griffin, James M., and Pirotte, Alain (2003) Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption. Empirical Economics 28, 795–811.10.1007/s00181-003-0161-9Google Scholar
Baltagi, Badi H., Bresson, Georges, and Pirotte, Alain (2008) To pool or not to pool? In Matyas, Laszlo and Sevestre, Patrick (eds.), The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice, pp. 517–546. Berlin: Springer-Verlag.10.1007/978-3-540-75892-1_16Google Scholar
Baltagi, Badi H. and Kao, Chihwa (2000) Nonstationary panels, cointegration in panels and dynamic panels: a survey. Advances in Econometrics 15, 7–51.10.1016/S0731-9053(00)15002-9Google Scholar
BP (British Petroleum) (2014) Statistical Review of World Energy 2014. Retrieved from http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.htmlGoogle Scholar
Brunnermeier, Smita B. and Levinson, Arik (2004) Examining the evidence on environmental regulations and industry location. The Journal of the Environment and Development 13, 6–41.10.1177/1070496503256500Google Scholar
Cole, Matthew A. and Elliott, Robert J. R. (2005) FDI and the capital intensity of “dirty” sectors: A missing piece of the pollution haven puzzle. Review of Development Economics 9, 530–548.10.1111/j.1467-9361.2005.00292.xGoogle Scholar
Copeland, Brian R. and Taylor, M. Scott (1994) North–South trade and the environment. Quarterly Journal of Economics 109, 755–787.10.2307/2118421Google Scholar
Copeland, Brian R. and Taylor, M. Scott. (2004) Trade, growth, and the environment. Journal of Economic Literature 42, 7–71.10.1257/.42.1.7Google Scholar
Duttaray, Mousumi, Dutt, Amitava K., and Mukhopadhyay, Kajal (2008) Foreign direct investment and economic growth in less developed countries: An empirical study of causality and mechanisms. Applied Economics 40, 1927–1939.10.1080/00036840600949231Google Scholar
Gray, Wayne B. (1997) Manufacturing Plant Location: Does State Pollution Regulation Matter? NBER working paper 5880.10.3386/w5880Google Scholar
Greene, William H. (2008) Econometric Analysis. New Jersey: Pearson.Google Scholar
Grether, Jean-Marie and De Melo, Jaime (2003) Globalization and Dirty Industries: Do Pollution Havens Matter? NBER working paper 9776.10.3386/w9776Google Scholar
Grossman, Gene M. and Krueger, Alan B. (1992) Environmental Impacts of a North American Free Trade Agreement. Discussion Papers in Economics no. 158, Woodrow Wilson School of Public and International Affairs.10.3386/w3914Google Scholar
Gurgul, Henryk and Lach, Lukasz (2014) Globalization and economic growth: Evidence from two decades of transition in CEE. Economic Modelling 36, 99–107.10.1016/j.econmod.2013.09.022Google Scholar
Hsiao, Cheng (2003) Analysis of Panel Data. Cambridge: Cambridge University Press.10.1017/CBO9780511754203Google Scholar
Hsiao, Cheng and Pesaran, M. Hashem (2008) Random coefficient models. In Matyas, Laszlo and Sevestre, Patrick (eds.), The Econometrics of Panel Data, pp. 185–213. Berlin, Heidelberg: Springer.10.1007/978-3-540-75892-1_6Google Scholar
Hsiao, Cheng, Pesaran, M. Hashem, and Tahmiscioglu, A. Kamil (1999) Bayes estimation of short-run coefficients in dynamic panel data models. In Hsiao, Cheng, Pesaran, M. Hashem, Lahiri, Kajal, and Lee, Lung Fei (eds.), Analysis of Panels and Limited Dependent Variable Models, pp. 268–296. Cambridge: Cambridge University Press.10.1017/CBO9780511493140.013Google Scholar
Hubbard, Timothy P. (2014) Trade and transboundary pollution: quantifying the effects of trade liberalization on CO2 emissions. Applied Economics 46, 483–502.10.1080/00036846.2013.857000Google Scholar
Im, Kyung So, Pesaran, M. Hashem, and Shin, Yongcheol (2003) Testing for unit roots in heterogeneous panels. Journal of Econometrics 109, 53–74.10.1016/S0304-4076(03)00092-7Google Scholar
Kar, Saibal and Majumdar, Devleena (2016) MFN tariff rates and carbon emission: Evidence from lower-middle-income countries. Environmental and Resource Economics 64, 493–510.10.1007/s10640-015-9918-9Google Scholar
Lau, Lin-Sea, Choong, Chee-Keong, and Eng, Yoke-Kee (2014) Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: Do foreign direct investment and trade matter? Energy Policy 68, 490–497.10.1016/j.enpol.2014.01.002Google Scholar
Levin, Andrew, Lin, Chien-Fu, and Chu, Chia-Shang James (2002) Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1–24.10.1016/S0304-4076(01)00098-7Google Scholar
Levinson, Arik and Taylor, M. Scott (2008) Unmasking the pollution haven effect. International Economic Review 49, 223–254.10.1111/j.1468-2354.2008.00478.xGoogle Scholar
List, John A. and Co, Catherine Y. (2000) The effects of environmental regulations on foreign direct investment. Journal of Environmental Economics and Management 40, 1–20.10.1006/jeem.1999.1095Google Scholar
Long, J. Scott (1997) Regression Models for Categorical and Limited Dependent Variables. London: Sage Publications.Google Scholar
Low, Patrick and Yeats, Alexander (1992) Do “dirty” industries migrate? In Low, Patrick (ed.), International Trade and the Environment. World Bank Discussion Papers, pp. 89–104. Washington, DC: World Bank.Google Scholar
Maddala, Gangadharrao S. and Hu, Wanhong (1996) The pooling problem. In Matyas, Laszlo and Sevestre, Patrick (eds.), The Econometrics of Panel Data: A Handbook of Theory with Applications, pp. 307–322. Boston: Kluwer Academic Publishers.10.1007/978-94-009-0137-7_13Google Scholar
Maddala, Gangadharrao S., Trost, Robert P., Li, Hongyi, and Joutz, Frederick (1997) Estimation of short-run and long-run elasticities of energy demand from panel data using shrinkage estimators. Journal of Business and Economic Statistics 15, 90–100.Google Scholar
Neumayer, Eric (2001) Pollution havens: Why be Afraid of International Capital Mobility? Presentation at Environmental Economics. The Netherlands: Jak Smulders and Erwin Bulte, Tilburg University.Google Scholar
Omri, Anis, Nguyen, Duc Khuong, and Rault, Christophe (2014) Causal interactions between CO2 emissions, FDI, and economic growth: Evidence from dynamic simultaneous equation models. Economic Modelling 42, 382–389.10.1016/j.econmod.2014.07.026Google Scholar
Palivos, Theodore and Varvarigos, Dimitrios (2017) Pollution abatement as a source of stabilization and long-run growth. Macroeconomic Dynamics 21, 644–676.10.1017/S1365100515000632Google Scholar
Pesaran, M. Hashem (2004) General Diagnostic Tests for Cross Section Dependence in Panels. CESifo working papers 1233.Google Scholar
Pesaran, M. Hashem (2007) A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics 22, 265–312.10.1002/jae.951Google Scholar
Pesaran, M. Hashem, Shin, Yongcheol, and Smith, Richard J. (1996) Testing for the Existence of a Long-Run Relationship. University of Cambridge DAE working paper 9622.Google Scholar
Pesaran, M. Hashem, Shin, Yongcheol, and Smith, Ron P. (1999) Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94, 621–634.10.1080/01621459.1999.10474156Google Scholar
Romer, Paul M. (1990) Endogenous technical change. Journal of Political Economy 98, 71–102.10.1086/261725Google Scholar
Sanna-Randaccio, Francesca and Sestini, Roberta (2012) The impact of unilateral climate policy with endogenous plant location and market size asymmetry. Review of International Economics 20, 439–656.10.1111/j.1467-9396.2012.01040.xGoogle Scholar
Shahbaz, Muhammad, Nasreen, Samia, Abbas, Faisal, and Anis, Omri (2015) Does foreign direct investment impede environmental quality in high, middle and low-income countries? Energy Economics 51, 275–287.10.1016/j.eneco.2015.06.014Google Scholar
Smith, Adrian F. M. (1973) A general Bayesian linear model. Journal of the Royal Statistical Society 35, 67–75.Google Scholar
Tobey, James A. (1990) The effect of domestic environmental policies on pattern of world trade: An empirical test. Kyklos 43, 191–209.10.1111/j.1467-6435.1990.tb00207.xGoogle Scholar
Trapani, Lorenzo and Urga, Giovanni (2009) Optimal forecasting with heterogeneous panels: A Monte Carlo study. International Journal of Forecasting 25, 567–586.10.1016/j.ijforecast.2009.02.001Google Scholar
UNCTAD (United Nations Conference on trade and Development) (2015) Handbook of Statistics. Retrieved from http://unctad.org/en/Pages/Publications/Handbook-of-Statistics.aspxGoogle Scholar
WEF (World Economic Forum) (2015) Global Competitiveness Report. Switzerland: World Economic Forum.Google Scholar
Westerlund, Joakim and Basher, Syed A. (2008) Testing for convergence in carbon dioxide emissions using a century of panel data. Environmental and Resource Economics 40, 109–120.10.1007/s10640-007-9143-2Google Scholar
Sullivan, Wilson, John, Tsunehiro, Otsuki, and Sewadeh, Mirvat (2002) Dirty Exports and Environmental Regulation: Do Standards Matter to Trade? Policy Research working paper 2806.10.1596/1813-9450-2806Google Scholar
Zarsky, Lyuba (1999) Havens, halos and spaghetti: Untangling the evidence about foreign direct investment and the environment. In OECD (ed.), Foreign Direct Investment and the Environment, pp. 47–73. Paris: OECD.Google Scholar