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The Sources of Heterogeneity in the Efficiency of Indian Pharmaceutical Firms

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Performance of Pharmaceutical Companies in India

Part of the book series: Contributions to Economics ((CE))

Abstract

Using the non parametric approach of Data Envelopment Analysis (DEA) this chapter examines firm’s heterogeneity in the Indian pharmaceutical industry by measuring their input and output efficiencies. The analysis establishes that even though firms have been able to make efficient use of inputs like labor and raw material, the output efficiency reveals a declining trend. The phenomenon can be attributed to differences in the size of firms and the presence of economies of scale in production. Further analysis reveals the importance of firm specific factors like its strategies and structure for variation in output efficiency. We find that firms that are vertically integrated with down-stream raw-material industry are more efficient. We also find that R&D is a possible strategic option for only the larger sized firms to gain higher efficiency.

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Notes

  1. 1.

    Since we have a single output case, the possibility of increasing each of the output in the output bundle at different proportions does not arise. While measuring the input specific efficiencies, we have, however, incorporated such possibilities.

  2. 2.

    For a detailed discussion on Data Envelopment Analysis see Ray (2004) and Charnes, Cooper, Lewin and Seiford, eds Data Envelopment Analysis: Theory, Methodology and Practice (1994).

  3. 3.

    See Ray (2004) for a detailed exposition on DEA.

  4. 4.

    Simar and Wilson (1998a, 2000) have set the foundation for using bootstrap techniques to generate empirical distributions of DEA efficiency scores and correct various randomness in DEA based estimates. The technique has, however, not tickled down to common practice because of the lack of availability of statistical packages.

  5. 5.

    According to the Pareto criteria, an input-output bundle cannot qualify as an efficient point if there remains the possibility of any increase in output or reduction in inputs.

  6. 6.

    For various properties of the input and output set and its relation with the technical efficiency of the firm one can refer to Varian (1984) and Ray (2004).

  7. 7.

    See Färe and Lovell (1978) and Russell (1985) for the limitation of the above measures.

  8. 8.

    The efficiency measure proposed by PRS is well defined, satisfies all the global properties of an efficiency measure compared to other non-radial measures. Additionally, it can also compute the input and output efficiencies of firms that other non-radial measures fail to capture. Further, Ray and Jeon (2007) has shown that the PRS measure of efficiency is a global generalized efficiency measure because all other radial and non-radial measures are a special case of PRS model.

  9. 9.

    Data generating process is a probability distribution that is supposed to characterize the population from which the data source has been drawn. It is the process by which the sample data is generated while the researcher estimates the statistical model of his interest. The set of data obtained depends crucially on the particular set of error terms drawn. A different set of error terms would create a different data set for the estimated statistical model (see Kennedy 1998; Härdle and Simar 1999 and so on for a detailed discussion on DGP).

  10. 10.

    The Monte-Carlo Stimulation carried out in the second stage indicates that the two stage method with DEA based efficiency in the first stage and OLS, maximum likelihood or even Tobit estimations, in the second stage performs far better than the parametric methods. The Banker and Nataranjan (2008) paper assumes a form of Data Generating Process (DGP) that is much more flexible and less restrictive than the one assumed by Simar and Wilson (2007) that has also examined the impact of contextual variables on the efficiency of firms in a two-stage process. While the Simar and Wilson (2007) paper argues that ML estimation of a truncated regression rather than the Tobit model is the preferred approach in the second stage, the Banker and Natarajan (2008) results are more robust and appropriate than the Simar and Wilson (2007) approach.

  11. 11.

    The advantage of panel data is its ability to account for the unobservable firm specific individual effects like managerial skill, firm-specific capabilities and others. Not accounting for the firm specific individual effects can actually lead to a bias in the resulting estimates (see Baltagi 2003).

  12. 12.

    As documented by Emrouznejad (2001), Tavares (2002) Gattoufi et al. (2004), and Cook et al. (2009) the total number of journal papers on efficiency analysis using only DEA exceeds 1,259. DEA methodology has also been widely applied mostly in the context of developing nations to evaluate the performance of public utilities like municipal corporations, education service providers, public sector and others and also for the traditional manufacturing sector. An internet search on a search engine like google.com on data envelopment analysis return about 1,000 hits, the vast majority of which appears to be working papers.

  13. 13.

    Although Kalirajan and Shand (1997) and Kalirajan and Obwana (1994) have integrated the random coefficient model of Swamy (1971) (that can handle the heterogeneity in slopes and intercept of firms) for econometrically estimating the frontier models and the input inefficiency of firms, such technique is not widely used because of computational difficulties.

  14. 14.

    The Prowess Data-Base provides firm level information from the year 1989 to the current year. However, data are consistently available only from the year 1991. Therefore, the study period from 1991 to 2005 has been considered in this paper. Also most of the policy changes for this sector were implemented between the year 1995 and 1998.

  15. 15.

    The figures have been arrived at by taking the ratio of the output manufactured by the registered Indian pharmaceutical companies (provided by the CMIE prowess database) to the total value of output produced by the sector (provided by the Ministry for Chemicals and Petro-Chemicals).

  16. 16.

    A value of 0.863 for labor efficiency implies that labor inefficiency is about 0.177. This is computed by deducting 0.863 from unity.

  17. 17.

    Discussion with companies reveals that a firm has to install high quality capital stock worth three crores to fulfill the requirement of the Schedule M of the newly amended Drugs and Cosmetic Act. Moreover, many firms have also upgraded their production system at par with the standard set by the regulatory body of the developed countries to export their product there. Since, return from capital stock generally takes time to realize, it may not be possible for a firm to realize fully the potential benefit of the capital stock at least in the short run.

  18. 18.

    We have also tried to estimate a regression model each for the input as well as the total input efficiency scores of the firms. Since the predictability powers of the models were low, we concentrate here primarily on output efficiency scores. Intuitively also it makes more sense to consider only output efficiency scores in our regression model because we notice that, on an average, firms are input wise efficient.

  19. 19.

    A number of studies have documented that because of various forms of entry barrier only the most productive firms self select for the global market.

  20. 20.

    See World Bank Report (1993, 1997) about a firm’s import of foreign technology and its positive impact on their efficiency.

  21. 21.

    Capital–Labor Ratio = Expenditure on plant-Machinery, Building and other fixed Asset adjusted for historic prices by employing PIM/Expenses for salaries and wages.

  22. 22.

    For the robustness of our results, we have estimated the econometric model for various sub-samples of our data set and found that there are no significant differences in our result.

  23. 23.

    An interesting point to mention here is that a large number of pharmaceutical firms still sell their products in the domestic market. Though not reported we have differentiated the firms that sell their product in the domestic market from the firms that sell their product in the international one using a dummy variable. The result of our analysis confirms that firms targeting the international market are always better off.

  24. 24.

    Even the largest company of India Ranbaxy incurred a significant loss in 2008 by exporting in the US market because it failed to fulfill the US Food and Drug Administration (FDA) regulatory requirement (see Mint, June 6, 2009). Similar was the plight for Dr. Reddys Lab and Cipla.

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    Mainak Mazumdar

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Appendix A

Appendix A

Fig. A.1
figure 1

Growth of inputs and output

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Fig. A.2
figure 2

Growth of output and capital

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Mazumdar, M. (2013). The Sources of Heterogeneity in the Efficiency of Indian Pharmaceutical Firms. In: Performance of Pharmaceutical Companies in India. Contributions to Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2876-4_3

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