2017
DOI: 10.1016/j.jeem.2017.05.005
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Pollution Haven and Corruption Paradise

Abstract: In this paper, we propose a new perspective to analyze the impact of institutions, environmental standards, and globalization on relocations of polluting rms in countries with lax environmental regulation (called pollution havens). Via a simple theoretical extension from the Economic Geography literature, we characterize the main features of pollution havens: a good market access to high-income countries and corruption opportunities. Using structural and reduced-form estimations, we analyse these determinants … Show more

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Cited by 169 publications
(70 citation statements)
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References 43 publications
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“…The influence of government expenditures (administrative service, investment development and safeguard governance) on total factor productivity possess only one corruption threshold. Candau and Dienesch (2017) International European-controlled enterprises 2007–2010 Bilateral trade; Corruption Index; Inflation; Environmental Regulation; GDP; Fixed Effects Regressions Corruption reduces environmental standards Biswas et al. (2012) 100 countries 1999–2005 Sulfur Emissions; GDP; Corruption index; T; Energy Efficiency; U; P; Shadow Economy Fixed Effects Regressions A shadow economy and level of emissions are largely influenced by the level of corruption.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…The influence of government expenditures (administrative service, investment development and safeguard governance) on total factor productivity possess only one corruption threshold. Candau and Dienesch (2017) International European-controlled enterprises 2007–2010 Bilateral trade; Corruption Index; Inflation; Environmental Regulation; GDP; Fixed Effects Regressions Corruption reduces environmental standards Biswas et al. (2012) 100 countries 1999–2005 Sulfur Emissions; GDP; Corruption index; T; Energy Efficiency; U; P; Shadow Economy Fixed Effects Regressions A shadow economy and level of emissions are largely influenced by the level of corruption.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In this regard, there is greater empirical literature that has established the reciprocal influence of corruption and the natural environment ( Akhbari and Nejati, 2019 ; Sinha et al., 2019 ; Wang et al., 2020 ; Candau and Dienesch, 2017 ). As such, evidence supports that corruption is one of the determining variables which impacts the procedure of natural environment preservation and lessening this cause has much influence on the rate of green economic development along with sustainability.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, if corruption is neglected, the impact of FDI on the environment will be erroneously estimated, and the corresponding environment protection policies will fail to achieve the desired results. To the best of the authors’ knowledge, Candau and Dienesch’s [ 69 ] is the only previous study that investigated this issue, and this study did not take into consideration the spatial factor in the environmental pollution in different regions. Wind direction and water currents may make the environment of one region susceptible to the pollution and emissions of the neighbor regions, and the geographic concentration of FDI intensifies the spatial autocorrelation of pollution [ 11 , 70 , 71 ].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Barrell and Pain [75] considered that FDI can bring significant technological spillover effects to the host country, while Copeland and Taylor [76] believe that FDI does not bring technological advances, but makes the host countries a "pollution sanctuary" for multinational companies. We selected the degree of openness as the fourth control variable, which was measured as the proportion of foreign direct investment in GDP; this expressed the degree of foreign trade, and tested whether the "pollution paradise" hypothesis exists in China [77]. The data originated from the China Statistical Yearbook.…”
Section: Variable Selection For the Spatial Durbin Model And Data Somentioning
confidence: 99%