Effect of firm ownership on productivity: empirical evidence from the Indian mining industry

Abstract

This paper empirically examines the difference in productivity existing between the public and private sector of the mining industry in India. The literature on the effect of firm ownership on productive efficiency stands highly divided; hence, in this context, our study adds to the literature by attempting to study the effect of firm ownership on total factor productivity (TFP) in the four sectors of the Indian mining industry from 2000 to 2016. Here, we have sought to compare the productivity difference between the public and private mining firms in the four sectors, namely metallic, non-metallic, coal and petroleum. This paper uses the Levinson and Petrin (LP) method for estimating the TFP of each firm. The results indicate the superiority of private firms in three sectors—metallic, non-metallic, and coal—whereas the petroleum sector reports quite the opposite result. The results raise questions about the stability of the public sector firms over time. Overall, the paper suggests measures like provision of incentives, improvement in infrastructural facilities, upgradation of manpower and so on to improve the productivity performance of public sector firms.

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Fig. 1
Fig. 2

Notes

  1. 1.

    We use the term state-owned enterprises (SOE) and public firms interchangeably in this paper.

  2. 2.

    Reporting mine refers to a mine reporting production or reporting ‘nil’ production during a year but engaged in developmental work such as overburden removal, underground driving, winzing and sinking work as well as exploration by pitting, trenching or drilling as evident.

  3. 3.

    For more information on the challenges that the Indian industry is inflicted with, refer to Parida and Madheswaran (2018) WP No-430.

  4. 4.

    For more information on the problems associated with the estimation of production function and the evolution of methodologies to address the problems, refer to Griliches and Mairesse (1998).

  5. 5.

    Refer to Griliches and Mairesse (1998) to have a comprehensive idea about the superiority of the semiparametric method over other methods like IV and GMM. Also see Biesebroeck (2007) for the details of alternative methods of measuring TFP.

  6. 6.

    Prowess is an online database provided by the Centre for Monitoring Indian Economy (CMIE) which covers financial data for over 23,000 companies operating in India. Most of the companies covered in the database are listed on stock exchanges, and the financial data includes all those information that operating companies are required to disclose in their annual reports. The accepted disclosure norms under the Indian Companies Act, 1956, make it compulsory for companies to report all heads of income and expenditure, which account for more than 1% of their turnover. The prowess database provides quantitative information on variables such as output, sales, raw materials and energy.

References

  1. Abramovitz M (1956) Resource and output trends in the United States since 1870. Am Econ Rev 46(2):5–23

    Google Scholar 

  2. Alchian (1965) Some economics of property rights. II Politico 30:816–829

    Google Scholar 

  3. Alchian AA, Demsetz H (1972) Production, information costs and economic organizations. Am Econ Rev 62(5):777–795

    Google Scholar 

  4. Arellano M, Stephen B (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Studies 58(2):277–297

    Article  Google Scholar 

  5. Averch H, Johnson LL (1962) Behaviour of the firm under regulatory constraint. Am Econ Rev 52:1052–1069

    Google Scholar 

  6. Biesebroeck JV (2007) Robustness of productivity estimates. J Ind Econ 55(3):529–569

    Article  Google Scholar 

  7. Boardman Anthony E, Vining AR (1989) Ownership and performance in competitive environments: a comparison of the performance of private, mixed, and state-owned enterprises. J Law Econ 32(1):1–33

    Article  Google Scholar 

  8. Buchanan JM (1978) The economics of politics. Institute of Economic Affairs, London

    Google Scholar 

  9. Caves DW, Christensen LR (1980) The relative efficiency of public and private firms in a competitive environment: the case of Canadian railroads. J Polit Econ 88(5):958–976

    Article  Google Scholar 

  10. Chamberlain G (1982) Multivariate regression models for panel data. Journal of Econometrics 18:5–46

    Article  Google Scholar 

  11. Das A (2012) Who extracts minerals more efficiently- public or private firms? A study of Indian mining industry. Journal of Policy Modelling 34(5):755–766

    Article  Google Scholar 

  12. Dewenter KL, Malatesta PH (2001) State-owned and privately owned firms: an empirical analysis of profitability, leverage, and labour intensity. Am Econ Rev 91(1):320–334

    Article  Google Scholar 

  13. Ehrlich I, Gallais-Hamonno G, Liu Z, Lutter R (1994) Productivity growth and firm ownership: an analytical and empirical investigation. J Polit Econ 102(5):1006–1038

    Article  Google Scholar 

  14. Fama EF (1980) Agency problems and the theory of the firm. J Polit Econ 88(2):288–307

    Article  Google Scholar 

  15. Faria RC, da Silva Souza G, Moreira TB (2005) Public versus private water utilities: empirical evidence for Brazilian companies. Econ Bull 8(2):1–7

    Google Scholar 

  16. Government of India (2016) Mining sector Achievements Reports. Ministry of Mines, New Delhi

  17. Griliches Z, Mairesse J (1998) Production functions: the search for identification. In: Griliches Z (ed) Practicing econometrics, essays in methods and applications. Edward Elgar, Cheltenham

    Google Scholar 

  18. Indian Bureau of Mines (2016) India mineral years book Indian Bureau of Mines: Ministry of Mines, New Delhi

  19. Jensen MC, Meckling WH (1976) Theory of the firm: managerial behaviour, agency costs and ownership structure. J Financ Econ 3(4):305–360

    Article  Google Scholar 

  20. Laffont J-J, Tirole J (1993) A theory of incentives in procurement and regulation. MIT Press, Cambridge

    Google Scholar 

  21. Levinsohn J, Petrin A (2003) Estimating production functions using inputs to control for unobservable. Review of Economic Studies 70:317–341

    Article  Google Scholar 

  22. Li S, Xia J (2008) The roles and performance of state firms and non-state firms in China’s economic transition. World Dev 36(1):39–54

    Article  Google Scholar 

  23. Marshak J, Andrews W (1944) Random simultaneous equations and the theory of production. Econometrica 12:143–205

    Article  Google Scholar 

  24. Martin S, Parker D (1995) Privatization and economic performance throughout the UK business cycle. Manag Decis Econ 16(3):225–237

    Article  Google Scholar 

  25. Megginson WL, Netter JM (2001) From state to market: a survey of empirical studies on privatization. J Econ Lit 39(2):321–389

    Article  Google Scholar 

  26. Niskanen W (1971) Bureaucracy and representative government. Aldine, Chicago

    Google Scholar 

  27. Olley GS, Pakes A (1996) The dynamics of productivity in the telecommunication equipment industry. Econometrica 64(6):1263–1297

    Article  Google Scholar 

  28. Olson M (1965) The logic of collective action: public goods and the theory of groups. Harvard University Press, Cambridge

    Google Scholar 

  29. Parida M, Madheswaran S. (2018) The Indian mining industry: present status, challenges and the way forward. Working paper N0.430, Institute for Social and Economic Change, Bengaluru

  30. Pollitt MG (1995) Ownership and performance in electric utilities: the international evidence on privatization and efficiency. Oxford University Press, Oxford

    Google Scholar 

  31. Reserve Bank of India (2017) Handbook of statistics on Indian economy. Reserve Bank of India, Mumbai

    Google Scholar 

  32. Srivastava V (1996) Liberalisation, productivity and competition: a panel study of Indian manufacturing. Oxford University Press, New Delhi

    Google Scholar 

  33. Stigler G (1971) The theory of economic regulation. Bell J Econ 2:3–21

    Article  Google Scholar 

  34. Strategy Paper in Mines (2011) Unlocking the potential of Indian mineral sector. Government of India

  35. Vickers J, Yarrow G (1988) Privatization: an economic analysis. MIT Press, Cambridge

    Google Scholar 

  36. Williamson OE (1964) The economics of discretionary behaviour: managerial objectives in a theory of the firm. Prentice Hall, Englewood Cliffs

    Google Scholar 

  37. Wolf C (2009) Does ownership matter? The performance and efficiency of state oil vs private oil (1987-2006). Energy Policy 37:2642–2652

    Article  Google Scholar 

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Appendix

Appendix

Measurement of capital

The database provides firms’ gross fixed asset (GFA) at historical cost. In many of our studies, especially in productivity-related studies, we use capital as a variable. But, it is a herculean task to estimate the capital accurately. Generally, capital stock is valued at historic cost which is known as book value of capital. But we actually need the present value of capital stock at constant prices for analysis. One of the methods to obtain this present value is through applying perpetual inventory method (PIM). Thus, in this study, data on capital stock have been estimated using the perpetual inventory method. As a first step to this, first of all, given GFA values are re-valued at the replacement cost with 2009–2010 as the benchmark year. For estimating the re-valuation factor, we have followed the method of Srivastava (1996). This study uses the gross fixed asset rather than the net fixed asset because depreciation charges in the Indian industry are highly arbitrary, fixed by income tax authorities hardly representing actual consumption. The detailed methodology of capital stock estimation (GFA) is presented below.

Estimation of capital stock using PIM

To estimate the capital stock from GFA, we have assumed the following:

  1. 1.

    Selection of the base year

    For our data, the base year is 2009 (our study period is 2000 to 2016). This is due to the availability of a greater number of observations in this year. We assume that the earliest vintage in the capital occurs from 1984, or the year of incorporation if it is after 1984. This specific year 1984 was chosen because the life of machinery and tools is assumed to be 25 years for the mining industry.

  2. 2.

    We assume that the price of capital changes at a constant rate  = Pt/Pt − 1 from 1984 or the year of incorporation up to 2009. The values of ∏ are arrived at from a series of price deflators constructed from CSO’s data on gross fixed capital formation (GFCF) published in various issues of the National Accounts of Statistics (NAS).

  3. 3.

    Similar to the price of capital, we also assume that the price of investment changes at a constant rate g = It/It − 1. The growth rate of fixed capital formation at 2004–2005 constant prices, taken from various issues of NAS, is applied to the case of all the firms.

  4. 4.

    Based on the value of ∏ and g, we estimate the ‘re-valuation factor’ RG, defined by Srivastava (1996) as

    $$ {R}^G=\left[{\left(1+g\right)}^{t+1}-1\right]\left(1+\prod \right)\left[\left(1+g\right)\left(1+\prod \right)-1\right]/g\left\{{\left[\left(1+g\right)\left(1+\prod \right)\right]}^{t+1}--1\right\} $$
  5. 5.

    After the re-valuation factor is estimated, we multiply the capital stock in the base year (2009) by this factor in order to convert the base year capital into capital stock at replacement cost at current prices.

  6. 6.

    The value of the capital stock in the benchmark year is then converted to constant prices using the WPI for machinery with the year 2004–2005 as the base.

  7. 7.

    Capital stock in the subsequent year is then estimated by adding the subsequent year’s investment, GFAt – GFAt − 1 (at constant prices), to the existing stock of the capital stock at each point of time using the perpetual inventory method.

Table 10 Sector-wise sample number of firms in the mining industry
Table 11 Percentage share of prowess output in the total value of mining output
Table 12 Summary statistics of the variables used in the analysis
Table 13 Weighted average of the TFP levels for the mining industry and its four subsectors

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Parida, M., Madheswaran, S. Effect of firm ownership on productivity: empirical evidence from the Indian mining industry. Miner Econ 34, 87–103 (2021). https://doi.org/10.1007/s13563-020-00223-6

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Keywords

  • Firm ownership
  • TFP
  • Levinson and Petrin method
  • Mining industry
  • Multivariate regression model