Eco-innovation and exports in heterogeneous firms: pollution haven effect and Porter hypothesis as competing theories

ABSTRACT

The effects of environmental policies on eco-innovation and trade performance are studied separately in the literature, and varying inferences across the studies are reported. This paper sheds light on this debate as it theoretically and empirically studies the pollution haven effect and strong Porter hypothesis in a unified framework that accounts for productivity and size heterogeneity at the firm level. The present study discusses a detailed analysis of theoretical predictions and empirical outcomes, based on the regulation–innovation–trade nexus, to assess the specific channels through which such effects might operate. Based on German and East European cross-sectional data at the firm level, results show that an eco-innovation that a regulation induces can generate either a positive effect or a detrimental effect on exporting propensity. Results also suggest that productivity, size and geographical heterogeneity of firms are extremely relevant.

KEYWORDS:

• Eco-innovation and environmental regulation nexus
• pollution haven effect
• Porter hypothesis
• exporting propensity
• firm heterogeneity

JEL CODES:

• F18
• F23
• Q55
• Q56

Acknowledgements

The authors want to thank the journal managing editor and the two referees for very helpful comments and suggestions. Moreover, the authors are very grateful to Prof. Alessio D’Amato (University of Rome – Tor Vergata), Prof. Giulia Bettin (Università Politecnica delle Marche), Prof. Giovanni Marin (University of Urbino Carlo Bo), Prof. Valeria Costantini (University of Rome – RomaTre), Prof. Christophe Charlier (Université Côte d'Azur) for precious insights received at the following conferences: 26th Annual Conference of the EAERE 2021; 60th Annual Scientific Conference 2019 – SIE (Economists Italian Society); 16th European Network on the Economics of the Firm (ENEF) meeting 2019; 7th IAERE Conference 2019. The usual disclaimers apply.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 See Mulatu (2018) for a comprehensive review on environmental regulation and international competitiveness.

2 A positive connotation also characterizes FDI because they drive the cross-country knowledge diffusion of environmental innovation. The literature has demonstrated that for this specific kind of innovation, knowledge transfer is guided by horizontal linkages (FDI), patenting and joint R&D activities (Gallagher 2014). Furthermore, environmental knowledge spillovers are contingent on firms located in nearby countries that already interact and could be connected to vertical linkages. Precisely, if a multinational enterprise adopts an internal policy that fosters environmental technologies adoption, firms integrated into its value chain learn to comply with it as well as if they are located abroad (Ning and Wang 2018).

3 The PHE is a driver of Pollution Haven Hypothesis (PHH), which underlines that trade liberalization can induce a reallocation of production: more polluting industries or firms move toward countries with a less stringent regulation (Copeland and Taylor 2004). Despite this work disregards the PHH, it is good to know that the PHE is a necessary, but not sufficient, condition for PHH. It is a sufficient condition when it dominates the other sources of comparative advantage (factor endowments and technological differences) (Cherniwchan, Copeland, and Taylor 2017).

4 Other two types of PH have been defined by Jaffe and Palmer (1997). The weak version suggests that command-and-control environmental regulation affects the adoption of ‘certain types' of innovation, mainly eco-innovation, but they cannot completely offset regulation compliance costs. The narrow PH points out the relevance of more flexible environmental policies, which have a higher impact on the adoption of innovation than command-and-control ones. These regulations also stimulate firms’ competitiveness.

5 It is exogenous at the firm level and endogenous at the industry level.

6 Nevertheless, firms in the data samples could operate under multiple markets, thus they may implement more factors of production other than labor, such as capital, the database does not collect any information about this input.

7 They are expressed in units of labor.

8 Differently from Copeland and Taylor (1994), we have drawn a simplified framework that considers only one factor of production and an exogenous environmental regulation because it is micro-founded. Our simplification allows us to pay more attention to the choice of technology and to analyse firms’ differences in terms of innovation.

9 It is obtained by solving the equation ${\pi }_d+{\pi }_d^{\ast }={\pi }_c+{\pi }_c^{\ast }$.

10 A detailed analysis of industry equilibrium is reported in Appendix 1.

11 For example, assuming a Pareto distribution, positive and negative effects will exactly balance with each other.

12 The sample of Eastern European countries includes Bulgaria, Croatia, Hungary, Lithuania, Latvia, Romania and Estonia.

13 See Tables A1 and A2 in Appendix 2 for sector description.

14 Technically, this measure is originally drawn as an ordered variable, but we transform it into a dichotomous one. Firms can choose among four degrees of importance of the regulation in introducing innovation: 0 not important, 1 low importance, 2 medium importance, 3 high importance. For our analysis, the degree equals 1 if firms answer 1, 2 or 3, and 0 otherwise.

15 The model is similar to a bivariate probit regression, but it differs in terms of variance. In bivariate probit, variances are set to 1, while no specific parameters are identified for these values in the endogenous switching model. Nichols (2011) has demonstrated that the bivariate probit model requires strong parametric assumptions, so it is not suitable if endogeneity of other variables are suspected and it cannot properly manage the overdispersion of data.

16 Concerning trade performance, as a possible competitiveness measure, several variables have been used and tested in firm level empirical studies. For example, Rammer et al. (2017) contribute by measuring export performance through two variables: exports on total sales at the end of a period and a dummy variable for export activities in the last period.

17 According to the existing literature on international trade with heterogeneous firms, this variable has been interpreted as economic performance. As generally asserted, international trade propensity is strictly related to productivity at firm level, so only the most productive firms may serve foreign markets.

18 See Table A3 in Appendix for detailed variable description.

19 See Table A9 and A10 in Appendix 2 for detailed results of the instrumental variables test.

20 The results connected to productivity have been also confirmed by the application of a non-parametric approach. Differences between productivity distributions will be tested through Kolmogorov–Smirnov test and Kruskal–Wallis test.

21 By analysing our samples, 55.9% of the German sample is represented by medium/large firms while, if we consider the EE sample, the share of medium/large firms equals 42.26% of the sample.

22 For this estimation, coefficients are not reported but are available upon request.

23 In this case, it is true that $\mathrm{\Phi }\left(-c_2\right)=1-\mathrm{\Phi }\left(c_2\right)$ and $\varphi \left(c_2\right)=\varphi \left(-c_2\right)$.

Additional information

Funding

This work is supported by the Department of Economics and Management of the University of Ferrara, through the research fellow funded by the Ministero dell'Istruzione, dell'Università e della Ricerca in the context of ‘Ludi Dipartimentali – Dipartimenti universitari di eccellenza’ call, and the Department of Economics, Society, Politics of the University of Urbino Carlo Bo, by a research fellow in the project ‘Innovation and global challenges in an interconnected world: the role of local resources and socio-economic conditions' funded by the Ministero dell'Istruzione, dell'Università e della Ricerca within the context of the Research Project of Relevant National Interest (PRIN) 2017.