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Is there an environmental benefit to being an exporter? Evidence from firm-level data

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Abstract

One concern over globalisation is its impact on the environment. We analyse the consequences of becoming an exporter on a firm’s energy consumption. We show theoretically and empirically with firm-level data that the increase in energy use when exporting is negatively correlated with energy intensity. This is because, although energy use rises with exporting due to greater production and transportation, it can be offset by adopting more energy-efficient technology. This second effect is strongest for high energy intensity firms. As such, analysis of average effects, as in other studies, conceals important connections between the trade and the environment.

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Notes

  1. This is similar to the aggregate data studies by Eskeland and Harrison (2003) and Cole et al. (2008). The existing firm-level studies of Galdeano-Gómez (2010), Girma et al. (2008) and Kaiser and Schulze (2003) consider the impact of exporting status on, respectively, a firm’s environmental productivity performance, adoption of pollution abatement technology and environmental expenses as alternative measures of environmental performance. In contrast, we examine the impact of exporting on actual energy use.

  2. For an overview of the empirical findings see Wagner (2007).

  3. Unlike our model the general equilibrium model of Bustos (2011) only has one factor of production and therefore does not consider the impact of factor intensity.

  4. When using a pooled sample, comparable to other studies, we find no significant effect of exporting on energy intensity. These results are available on request.

  5. Here we rely on quantile regression technique as used before in some trade literature, such as Yasar et al. (2006) and Girma and Görg (2005).

  6. Matching in combination with difference in differences has been widely used. Amongst the studies used to analyse an effect of exporting on productivity using matching and difference in differences are Girma et al. (2004) and Wagner (2002).

  7. This is akin to the multi-factor model of Bernard et al. (2007). In practice, we would expect the largest differences in α(i)'s to be found across sectors, with some industries more energy-intensive than others. This does not negate, however, the potential for firms within an industry to vary according to energy intensity as some firms, through design or happenstance, may utilize different production methods than others in the same industry.

  8. This is akin to the iceberg transport costs common to these models.

  9. In addition, it is common to assume a cost to learning one’s b(i) and α(i). Since this does not affect relative payoffs from the different choices, we ignore it here.

  10. In line with the heterogeneous firms literature, we assume that parameters are such that any exporting firm also chooses to serve the domestic market. Assuming positive transport costs and/or that F X  > F are sufficient for this.

  11. Similar figures are found if we alter the production function to be \(b(i) \alpha_i ^{-\alpha_i} (1-\alpha_i)^{\alpha_i-1}l^{1-\alpha_i} (t_{j}f)^{\alpha_i}\), a production function in which the cost function depends on α only in the exponents on wages and energy prices. When these prices are equal, this alternative production function yields profits that are linear in α. However, the relative ranking of technology choices remains the same, i.e. high α firms are more likely to use the high technology than low α firms regardless of export status, and exporters are more likely to adopt the high technology than non-exporters for a given α.

  12. This provides a potential benefit to the environment from trade since highly productive exporters drive out low productivity domestic firms. If this results in a greater percentage of firms using a more environmentally friendly technology, this could lead to a positive correlation between international trade and the environment. We leave a thorough treatment of this issue to future research.

  13. The list of industries is given in Table 8 in the “Appendix”.

  14. To prepare the data prior to analysis, we were required to clean the data. In a few instances, the CIP data reported negative or missing values of energy and/or export share and/or zero values of employment, earnings and/or turnover. When possible, these were replaced using values from adjacent years. When this was not possible, the observation was dropped. For instances of export shares bigger than 100 their values were replaced using values from previous and later years. Export share values that could not have been replaced were treated as follows. Firms which did not have an export share equal to 100 in any other years were dropped from the sample. If a firm had at least one occurrence of export share equal to 100 in other years the value of export share larger than 100 was set to 100.

  15. Monetary values are deflated using Industrial Producer Price Indices with year 2000 as a base, provided by the CSO. Energy variables are deflated using the CSO Wholesale Price Indices for Energy Products with year 1995 as a base.

  16. Although not reported here, exporters are also on average more R&D intensive.

  17. The same results are obtained when using energy relative to total costs or absolute energy usage as alternative measures of a firm’s energy intensity.

  18. Results for 0.10th quantile are not reported to save space but the exporter effect is positive in line with theoretical predictions.

  19. Statistical significance of this result generally disappears when removing outliers from the data set.

  20. We cannot include firm-level fixed effects in a probit estimation as it leads to inconsistent estimates, see Wooldridge (2002, p. 484).

  21. See Caliendo and Kopeinig (2005) for further details on the quality of matching.

  22. A caveat should be mentioned here that although matching with difference in difference allows us to control for both observable firm level characteristics and time-invariant unobservables, it is possible that subject to data limitations some time-varying firm characteristics might be omitted.

  23. The choice of variables used is a combination of their significance and quality of matching. Tables 11, 12 and 13 in the “Appendix” assess the quality of matching by reporting t-tests that indicate that there are no statistically significant differences in the means of variables used to calculate the propensity scores.

  24. It is important to note here that these results are not derived from matching firms on the extreme ends of the distribution. Rather, results for the low energy intensity are derived from firms at slightly above the 0.20 percentile of energy intensity, while results for the high energy intensity firms are for those firms clustered at around the 0.90 percentile.

  25. A caveat should be mentioned with regard to the results for firms that stop exporting. Due to CSO data collection issues, the results for the high energy intensity firms stopping exporting might be affected, although, if anything, we expect a bias towards zero outcomes.

  26. Note that here firms are divided into lower and upper quantiles of the sum of energy and freight costs relative to turnover rather than energy costs alone.

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Authors and Affiliations

  1. School of Economics, University College Dublin, Dublin, Ireland

    Svetlana Batrakova & Ronald B. Davies

Authors
  1. Svetlana Batrakova
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  2. Ronald B. Davies
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Correspondence to Svetlana Batrakova.

Additional information

This paper uses confidential micro data set of the Central Statistics Office Ireland (CSO). The restricted and controlled access to the data was provided in accordance with the Statistics Act, 1993. We are very grateful to Kevin Phelan, Dan Lawlor and Jane O’Brien for the provided assistance. We thank Stefanie Haller, Paul Devereux, Fergal McCann, participants at the 2010 Spring Midwest International Trade Meeting, at the ESRI Research Seminar, Xth RIEF Meeting, at the 24th Irish Economic Association Conference and the editor and one anonymous referee for helpful comments and suggestions. Funding from the Irish Research Council for the Humanities and Social Sciences (IRCHSS) and the Irish Research Council for Science, Engineering and Technology (IRCSET) is gratefully acknowledged. This paper is produced as part of the project “Globalization, Investment and Services Trade (GIST) Marie Curie Initial Training Network (ITN)” funded by the European Commission under its Seventh Framework Programme—Contract No. FP7-PEOPLE-ITN-2008-211429.

Appendix

Appendix

See Tables 7, 8, 9, 10, 11, 12, and 13.

Table 7 Baseline values for simulations
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Table 8 List of NACE 2-digit industries in the census of industrial production (CIP)
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Table 9 Definition of variables
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Table 10 Summary statistics, manufacturing
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t-tests for Sect. 5.2 comparing sample means of the treated and control groups to assess the quality of propensity score matching performed. Both tables indicate that there is no statistically significant difference in the means of variables used to calculate the propensity score.

Table 11 All manufacturing firms, t-test
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Table 12 Low energy intensity firms, t-test
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Table 13 High energy intensity firms, t-test
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Batrakova, S., Davies, R.B. Is there an environmental benefit to being an exporter? Evidence from firm-level data. Rev World Econ 148, 449–474 (2012). https://doi.org/10.1007/s10290-012-0125-2

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