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Measuring technical efficiency and shadow price of water pollutants for the leather industry in India: a directional distance function approach

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Abstract

This paper measures the cost of reducing pollution from the Kanpur leather industry which is a prime source of pollution in India’s largest river basin of Ganges. The study uses directional distance function approach to examine the efficiency of leather firms in abating two undesirable pollutants (total suspended solids and chromium) while expanding the desirable leather output, and provides robust estimates of the marginal abatement cost for different production and pollution abatement strategies. The study is based on the primary data collected for 61 firms in Kanpur leather cluster for the year 2016. The results show that leather firms are technically inefficient and incur high abatement cost under the existing command and control regulations. The least inefficient strategy is a balanced policy that allows firms to reduce pollution without compromising their goal of output expansion. The study finds that old, small and more pollution intensive firms can abate pollution at least cost under a market-based regime. The shadow price of pollutants estimated in this paper are useful tools in determining equilibrium discharge permit price for design of market-based instruments.

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Notes

  1. In the paper, undesirable outputs refer to the pollutants generated in the production process while desirable outputs refer to the marketable outputs produced by the firms. Undesirable and desirable outputs are synonymously called bad and good outputs respectively.

  2. The conventional output distance function simultaneously expands the good and the bad outputs onto the production frontier (Färe et al. 2006).

  3. It is used as a tanning agent by majority of the leather firms. Exposure to chromium is associated with chronic diseases as its hexavalent form is carcinogenic.

  4. The Ganga Action Plan was introduced in 1986 with the aim to abate pollution in river Ganges to improve the water quality.

  5. The data on pollution load of firms is not readily available.

  6. This property is inherited from the multiplicative homogeneity property of output distance function (Fӓre et al. 2005; Wei et al. 2013).

  7. Wang et al. (2017) and Molinos-Senante et al. (2015) estimated shadow price of \({CO}_{2}\) for Iron and steel industry and wastewater treatment plants, respectively for China using similar DVs.

  8. The specification of the production technology constrains the shadow prices to be non-negative i.e. it precludes the downward sloping portion of the production frontier.

  9. This information was received from the office of UPPCB situated in Kanpur.

  10. Even though there is a permissible discharge (upper) limit on TSS and chromium, the concentration of pollutants varied among firms. About 30% of the 275 tanneries were found in non-compliance with the discharge standards of the pollutants.

  11. All input and output definitions are in accordance with ASI. The data on inputs and outputs pertain to tannery operation of a single firm.

  12. Mandays is calculated as the number of the workers employed multiplied by the total number of working days of a leather tannery in the given year.

  13. The parameters are estimated using the Scilab 5.5.2 software.

  14. The zero shadow price of chromium for DVs (1, 0) and (1, -1) indicates that the inefficient firms are projected on the horizontal portion of the production frontier where the slope of the frontier is zero, while the projection is on upward sloping portion of production frontier for DV (0, -1) yielding a positive shadow price of chromium (personal communication with Prof W.L. Weber, 2020).

  15. Previous studies have also found zero shadow price for one of the pollutants in case of multiple bad outputs. Example, Murty and Kumar (2002) found that BOD and COD had a positive shadow price while TSS had zero shadow price for water polluting industries in India. Fӓre et al. (1993) found zero shadow price of TSS for US paper and pulp mills, suggesting that TSS is jointly reduced with other effluents without any additional cost.

  16. We have converted shadow price of other studies from INR values to USD using exchange rate 1 USD = 69 INR.

  17. This is the average shadow prices of pollutants for the three DVs.

  18. In the previous studies, firms surveyed had their own individual treatment plants while in our sample, they are connected to a CETP and they only do a primary treatment at their primary effluent treatment plant. This is mainly because they are operating on a small scale and cannot afford their own individual treatment plants for the overall treatment of discharge.

  19. The Government of India announced demonetization of Rs 500 and Rs 1000 currency notes on 8th November 2016 to curtail the shadow economy and use of illicit money.

  20. Estimates from deterministic methods (like ours) are higher to stochastic methods as deterministic approach do not account for error term.

  21. Leather is a labor intensive industry therefore, we took labor intensity as one of the determinants to assess its impact on the shadow price.

  22. We have shown the relationship between shadow prices and firms’ characteristics for strategies where firms are required to reduce pollutants i.e. g = (1, -1) and g = (0, -1). Abatement cost of TSS is decreasing with size for g = (0, -1) but this impact is found insignificant in the regression analysis. Otherwise, size is positively associated with shadow prices of TSS and chromium.

  23. Kanpur leather industry is highly export oriented and competes with other Asian economies like China and Bangladesh in producing low cost leather for the international market.

  24. Size is defined by the maximum production capacity of firms in terms of producing hides. The small firms produce less than 50 hides a day, medium firms produce between 50 to 150 hides and large firms produce more than 150 hides a day. In our sample data, there are 18 small firms, 24 medium firms and 19 large firms.

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Acknowledgements

The first author would like to acknowledge the financial support from Indian Institute of Technology Bombay towards conducting the fieldwork and the doctoral research. We acknowledge the support from officers of Uttar Pradesh Pollution Control Board and Annual Survey of Industry in data collection in Kanpur. We also thank the anonymous reviewers who helped improve the paper with their valuable feedback.

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  1. Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    Aparajita Singh & Haripriya Gundimeda

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Correspondence to Aparajita Singh.

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Appendix 1

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Table 7 Parameter estimates of the directional distance function
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Singh, A., Gundimeda, H. Measuring technical efficiency and shadow price of water pollutants for the leather industry in India: a directional distance function approach. J Regul Econ 59, 71–93 (2021). https://doi.org/10.1007/s11149-020-09422-z

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