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National culture and eco-efficiency: an application of conditional partial nonparametric frontiers

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Abstract

Given that norms govern individual behaviour, which in turn are related to the environmental behaviour, this study provides empirical evidence of the link between human behaviour and the environment. Firstly with the use of robust frontiers the eco-efficiency ratios of 72 countries are constructed. Then, by applying probabilistic approaches, countries’ eco-efficiencies conditioned on their cultural values are estimated. In a second-stage nonparametric regression analysis, the effect of countries’ cultural values on their eco-efficiency levels is captured. The empirical results reveal that distinct cultural characteristics explain countries eco-efficiency variations.

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Notes

  1. Due to the fact that national culture is a total system (Hofstede 1980) of different environmental values, beliefs and perceptions among the countries will lead to different environmental policies and that will be reflected on their environmental performance levels.

  2. In this study we are using the term eco-efficiency instead of environmental performance, since our efficiency measurement is the ratio of value added to the environmental damage (or pressure index), approaching therefore environmental performance measurement from a social point of view (Kuosmanen and Kortelainen 2005; Kortelainen 2008).

  3. Following Hofstede (1980; p. 32) we adopt a system approach by which “any element of a total system called culture should be eligible for analysis, regardless of the discipline that usually deals with such elements. At the level of (national) cultures, these are phenomena on all levels: individuals, groups, organisations, or society as a whole”.

  4. Lovell (1993; p. 53) distinguishes the inputs/outputs of the production process as “variables under the control of the decision maker during the time period under consideration”, from explanatory variables (external factors) that are “variables over which the decision maker has no control during the time period under consideration”.

  5. An alternative to the order-m partial frontier is the order-α, quantile-type frontier introduced by Daouia and Simar (2007).

  6. A valid stochastic semi-nonparametric technique that can handle noise based on Kernel regression can be found in the works by Fan et al. (1996) and Kneip and Simar (1996) or in the recent developments of nonparametric least squares approach (Johnson and Kuosmanen 2011, 2012; Kuosmanen and Kortelainen 2012).

  7. For an interesting discussion, critique and different operationalizations of weak disposability see the works by Hailu and Veeman (2001), Färe and Grosskopf (2003), Hailu (2003), Kuosmanen (2005), Färe and Grosskopf (2009), Kuosmanen and Podinovski (2009) and Podinovski and Kuosmanen (2011). For an application of different modelling settings of the bad output see also Nakano and Managi (2012).

  8. For a discussion on the linear monotone decreasing transformation of undesirable outputs see Färe and Grosskopf (2004) and Seiford and Zhu (2005).

  9. As can be observed all the variables used for the construction of eco-efficiency are in per capita terms. However, it must be mentioned that the use of ratios as inputs and outputs in DEA settings might be problematic suggesting the use of convex technologies in DEA settings (Hollingsworth and Smith 2003).

  10. According to Managi et al. (2009) these data are superior to other datasets in terms of their spatial and temporal resolution.

  11. Countries cultural values can be found at: http://geert-hofstede.com/countries.html.

  12. See for instance the studies by McSweeney (2002), Hofstede (2002) and Minkov and Hofstede (2011).

  13. For information regarding the construction of national cultural indexes see Hofstede (1980), Hofstede et al. (2010), Minkov and Hofstede (2011).

  14. For the theoretical background and the asymptotic properties of nonparametric conditional efficiency measures see Jeong et al. (2010).

  15. For larger values of m the results converge very quickly to the full-frontier results (similar to FDH results).

  16. The calculation of bandwidth by Bădin et al. (2010) is based on the least squares cross-validation (LSCV) criterion introduced by Hall et al. (Hall et al. 2004) and Li and Racine (2007).

  17. However, it must be mentioned that a weakness of this approach is the inability to summarise the effect of the cultural factors to a single coefficient.

  18. We set a threshold value distance from 1 as 0.5. So all eco-efficiency scores >1.5 are treated as potential outliers (Simar and Wilson 2003). All the values >1.5 are presented in Table 2 in bold.

  19. Due to the enormous quantity of results obtained it is difficult for the results to be presented here. However, all results are available upon request.

  20. As explained earlier, we use the Nadaraya (1964) and Watson (1964) nonparametric regression estimator and the least squares cross-validation data driven method (Hall et al. 2004) for the bandwidth selection.

  21. As suggested by an anonymous reviewer, for the case of PDI we excluded from the nonparametric regression analysis three countries, which were acting as outliers. These countries are: Austria (Qz1 = 2.1248; PDI = 11), El Salvador (Qz1 = 2.0212; PDI = 66) and Estonia (Qz1 = 2.0148; PDI = 40).

  22. The dotted lines indicate the pointwise 95 % variability bounds (Hayfield and Racine 2008).

  23. Even we analyse only the statistical significant regressors, Fig. 1 presents the visualisation effect of all the cultural dimensions.

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Acknowledgments

We would like to thank Professor Shunsuke Managi and three anonymous reviewers for their helpful and constructive comments on earlier versions of our manuscript. Any remaining errors are solely the authors’ responsibility.

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Correspondence to George E. Halkos.

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Halkos, G.E., Tzeremes, N.G. National culture and eco-efficiency: an application of conditional partial nonparametric frontiers. Environ Econ Policy Stud 15, 423–441 (2013). https://doi.org/10.1007/s10018-013-0066-6

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