# Economic contribution of government department enterprises in India.

This study analyzes the productivity and production function in India s manufacturing sector with particular reference to performance of government department enterprises. The data source for the study is Annual Survey of Industries (ASI) of the Central Statistical Organization (CSO), Government of India and covered the period 2001-02/2012-13. Cobb-Douglas production function was applied to measure the productivity ratios and technical progress. Marginal productivity of labor varied between 0.157 units and 8.416 units across the years. These enterprises recorded marginal productivity of capital of 2.1862 units. The average capital intensity ratio was found to be 3.919. Organizational efficiency in the sector was found high.Introduction

The economic development of a country depends mainly on industrial development. In manufacturing sector, the scope for internal as well as external economies is greater than in the other sectors. The sector acts as an instrument both for creating capacity to absorb excess labor power and for diversifying the market required to boost economic development. Since the early 1990s, the role of the government department enterprises has undergone a rapid change. Integration of the domestic economy with global markets has thrown up a plethora of opportunities and challenges. Some of the enterprises with strategic vision are actively exploring new avenues and have increased their activities to go in for mergers, acquisitions, amalgamations, take over's and creating new joint ventures. The present study attempts to analyze the productivity and production function in India's manufacturing sector with particular reference to the performance of government department enterprises.

Selection of the Variables

Net Value Added (NVA) was taken as output, since trends are not affected significantly by the use of net value added. Also ambiguity in the calculation of depreciation can be overcome if net value added is taken as a measure of output.

Labor input consisted workers directly involved in production while fixed capital was taken into account as capital input. Wages included remuneration paid to workers.

Data Base

The data source of the study was the Annual Survey of Industries (ASI) published by the Central Statistical Organization (CSO), Government of India and covered the period 2001-02/2012-13. All the referred variables were normalized by applying Gross Domestic Product (GDP) deflator. The GDP at current and constant prices were obtained by referring to Economic Survey, published by Government of India, Ministry of Finance and Economic Division Delhi.

Cobb-Douglas Production Function

Production function approach to productivity measurement is more advantageous because it can handle the problems arising out of non-separability of inputs and output, non-neutral technical change, variable returns to scale and non-proportionality of input prices of their respective marginal productivity in an explicit manner. A production function shows the technological relationship between the maximum output obtained from a given set of inputs and between the inputs themselves in the existing state of technological change. In this approach to productivity measurement the components of productivity can be arrived at directly by econometric estimation. The production function can be used to measure the efficiency of production technology, returns to scale, the degree of economies of scale, the degree of capital intensity of technology and the degree of substitution between factors of production.

One of the most commonly estimated functional forms is the Cobb-Douglas production (C-D) function written as:

V = A (t)[K.sup.[alpha]] [L.sup.[beta]] [e.sup.u]

Where [alpha] and [beta] are the coefficients of labor and capital respectively, A (t) is the efficiency parameter and u is the stochastic disturbance term following usual properties. Before the production function can be estimated a functional form has to be given to the term A (t). The most commonly used in practice has been A (t) = Ae[lambda]t where [lambda] is the measure of technical change in output per period [[lambda] measures the proportionate change in output per period when input level are held constant]. It is very important here to point out the limitations of this representation of technical change. It assumes neutral technical progress and that it is exogenous and disembodied (this neglects the usefulness of investment for technical progress).

This function is linear in the logarithm of the inputs, output and time. Thus,

We have:

Ln V = a + [alpha] LnL + [beta]LnK + [lambda]t + [mu]i

The estimation of this equation yields values of [alpha], [beta], and [lambda], where [lambda] provides estimates of Total Factor Productivity Growth (TFPG) and is the rate of exponential technological change. Sum of the partial elasticities ([alpha] + [beta]) indicates the extent of economies or diseconomies to scale. The returns to scale are constant, increasing or decreasing if the value of [alpha] + [beta] is equal to unity, more than unity or less than unity respectively.

Marginal products of labor (MPL) and capital (MPK) can be obtained by applying the following formula:

[MP.sub.L] = [delta]V/[delta]L = [alpha]/L [M.sub.K] = [delta]V/[delta]K = [beta]V/K

Since profit maximization entails that marginal productivity of labor is equal to the real wage rate and marginal product of capital is the price per unit of capital, it would imply that:

[MP.sub.L] = w/p = [alpha]V/L or share of labor in total output:

[alpha] = (w/p)(L/V)

Similarly

[MP.sub.K] = r/p = (K/L)

Or share of capital in total output

[beta] = (r/p)(K/L)

Results & Discussion

The technical progress in these sectors was analyzed by calculating marginal productivity of labor ([MP.sub.L]), marginal productivity of capital ([MP.sub.K]), marginal rate of technical substitution of labor for capital ([MRT.sub.LK]) and capital intensity (K/L). Marginal productivity or co-efficient of capital ([MP.sub.K]) may be defined as the ratio between change in output in a given economy or industry for a given time period and change in gross block of that economy or industry. Marginal productivity of labor ([MP.sub.L]) may be defined as the ratio between a change in output in a given economy or industry for a given period and change in amount of labor use. Capital intensity K/L is nothing but the state of technology. The [MRTS.sub.LK] explains the rate at which substitution was taking place between labor and capital.

Growth of [MP.sub.L]

The trends in the growth of marginal productivity of labor ([MP.sub.L]) are presented in Table 1.

Average [MP.sup.L] ratio of government department enterprises during the period was 3.9543. Wide variations were observed during the period under study. This is evident from the co-efficient of variation (c.v). MPl ratio varied between 0.157 units and 8.416 units across the years. The variations in MPl ratios might be due to wage differentials across the time.

Growth of [MP.sub.K]

Table 2 presents details regarding [MP.sub.K] ratios from 2001-02 to 2012-2013.

The [MP.sub.K] ratios during the reference period were positive. This shows that capital contributed positively to output. These enterprises recorded the maximum productivity performance of 2.1862 units with maximum variation of 63.09 percent.

Growth of K/L

The capital intensity ratios (K/L) from 2001-02 to 2012-2013 are given in Table 3.

During the reference period the average capital intensity (K/L) ratio was found to be 3.919. The K/L ratios from the beginning of the period to the end had shown a decline from 4.850 to 3.9195, which shows that lower quantum of fixed assets had been accumulated for a given unit of labor.

Growth of [MRTS.sub.LK] Ratios

The estimated [MRTS.sub.LK] during the period 2001-02/2012/13 is presented in Table 4.

The [MRTS.sub.LK] ratios of government department enterprises during the period under study showed that all the ratios were positive. The mean [MRTS.sub.LK] was 3.2064. Across the years the growth of the ratios was not stable since the magnitude of variability was 95.69 percent.

Production Function Estimates

The estimated production function is presented in Table 5

Efficiency parameter 'A' is positive and statistically significant. The implication is that the organizational efficiency is high, positively contributes to output and its contribution was explicitly significant in output generation. Elasticity of capital with respect to output ([beta]1) is positive and is statistically significant. An encouraging feature noticed from the results is that wage coefficient is positive and statistically significant. This implied that wage contributes significantly to output. The sum of the coefficients imply that it had recorded increasing returns to scale. The percentage share of factor inputs presented in the table indicated that share of wages was higher than the share of capital. This implied that these enterprises were labor intensive in their operation.

Conclusion

Wide variations were observed in the growth rate of [MP.sub.L]. Capital contributed positively to output based on [MP.sub.K] ratios. Lower quantum of fixed assets had been accumulated for a given units of labor. The [MRTS.sub.Lk] across the reference period was not stable since the magnitude of variability was 95.69 percent. Development of higher infrastructural facilities in the form of power, roads and telecommunication facilities has to be a top priority for the policy makers to raise the productivity and efficiency of the factors used in these enterprises.

M. Manonmani is Professor of Economics, Avinashilingam Institute For Home Science & Higher Education For Women, Coimbatore. E-Mail: manomyil@yahoo.com

Table 1 [MP.sub.L] Ratios of Government Department Enterprises Year Ratios 2001-2002 5.26 2002-2003 4.365 2003-2004 3.524 2004-2005 0.736 2005-2006 7.89 2006-2007 3.156 2007-2008 6.417 2008-2009 2.84 2009-2010 0.157 2010-2011 8.416 2011-2012 0.736 2012-2013 4.891 Average 3.9543 Standard Deviation ([sigma]) 2.8378 Co-efficient of Variation (c.v) 71.76 Source: Calculations based on data from Annual Survey of Industries (ASI) Table 2 [MP.sub.K] Ratios of Government Department Enterprises Year Ratios 2001-2002 2.578 2002-2003 1.314 2003-2004 0.312 2004-2005 1.959 2005-2006 2.09 2006-2007 0.696 2007-2008 3.609 2008-2009 2.06 2009-2010 0.158 2010-2011 3.944 2011-2012 3.905 2012-2013 3.609 Average 2.1862 Standard Deviation ([sigma]) 1.3794 Co-efficient of Variation (c.v) 63.09 Source: Calculations based on data from Annual Survey of Industries (ASI) Table 3 K/L Ratios of Government Department Enterprises Year Ratios 2001-2002 4.850 2002-2003 7.916 2003-2004 1.333 2004-2005 2.538 2005-2006 4.541 2006-2007 9.833 2007-2008 8.529 2008-2009 1.513 2009-2010 1.569 2010-2011 1.556 2011-2012 1.762 2012-2013 1.176 Average 3.9195 Standard Deviation ([sigma]) 3.184 Co-efficient of Variation (c.v) 81.23 Source: Calculations based on data from Annual Survey of Industries (ASI) Table 4 [MRTS.sub.LK] RATIOS Year Ratios 2001-2002 2.04 2002-2003 3.321 2003-2004 0.341 2004-2005 0.375 2005-2006 0.377 2006-2007 3.852 2007-2008 1.002 2008-2009 4.934 2009-2010 0.315 2010-2011 8.812 2011-2012 4.641 2012-2013 8.566 Average 3.2064 Standard Deviation ([sigma]) 3.0685 Co-efficient of Variation (c.v) 95.69 Source: Calculations based on data from Annual Survey of Industries (ASI) Table 5 Estimates of Production Function Variables Coefficients A (constant) 10.835 (0.987) Capital ([beta]1) 0.46 ** (2.578) Wages ([beta]2) 0.578 (5.26) Economics of scale(S) 1.038 R2 0.66 D.W Statistics 0.834 Percentage Share of Capital ([beta]1/S) 44 Percentage Share of Labor ([beta]2/S) 56 Source: Calculations based on data from Annual Survey of Industries (ASI) Figures in parentheses are the t-values ** Significant at 5% level

Printer friendly Cite/link Email Feedback | |

Author: | Manonmani, M. |
---|---|

Publication: | Indian Journal of Industrial Relations |

Geographic Code: | 9INDI |

Date: | Jan 1, 2016 |

Words: | 1931 |

Previous Article: | ILO, WTO & Indian industrial relations. |

Next Article: | Development oriented training climate in institutions of higher education. |

Topics: |