Multifactor Productivity Growth and Capacity

Citation: Das, P. (2013), Multifactor Productivity Growth and Capacity Utilisation in Manufacturing Industries in India: Estimating Stochastic Frontier with Firm Level Data, in Sharma et al. (eds.), Econometric Application in Management, National Publishing House, Delhi

Multi Factor Productivity Growth and Capacity Utilisation in Manufacturing Industries in India: Estimating Stochastic Frontier with Firm Level Data Panchanan Das Associate Professor of Economics Department of Economics University of Calcutta Email: [email protected]

The objective of this paper is to estimate the sources of productivity growth in the Indian manufacturing industry with firm level data from Annual Survey of Industries (ASI) during the period 1998-2010 by applying stochastic frontier approach in a panel data frame. The regional dispersion of manufacturing growth in the factory sector has been clear in India. The composition of workforce engaged in producing units in the ASI sector has been dominated highly by the ordinary workers. Gross value added in real terms grew at a significantly higher rate than the rate of growth of conventionally measured productivity. The pattern of growth of different inputs suggests, although grossly, that the structural change in Indian manufacturing industries took place in favour of capital. The estimated output elasticity of capital was significantly higher than that of labour. However, ordinary workers contributed to value added at a higher rate as compared to other type of employees. The multifactor productivity (MFP) growth rate in manufacturing industries in India was moderate and was highly uneven across the major states. The regional variation in labour productivity growth was accompanied by the variation in MFP growth and capacity utilisation rate. JEL: C20, C24, O40 Key words: Stochastic frontier, Productivity, Capacity utilisation, India

1. Introduction India experienced faster economic growth, as conventionally measured under neoliberal reforms since the early 1990s. But the benefits of higher growth have been concentrated mainly in the country’s richer states, leaving the poorer states further and further behind (Das, 2007). As the manufacturing sector is viewed as a leader of job creation and productivity growth through positive spill over, the unequal incidence of development in industrial activities is largely accounted for some regions growing relatively fast and others tending to be left behind (Kaldor, 1981). In the context of new industrial policy, a part of neoliberal reforms as initiated by the government of India in the early 1990s, it is extremely 1|Page

important to look into the productive performance of the manufacturing sector at the state level during the period when states have got some flexibility in initiating their own industrial policy.

Productivity growth perhaps is the most important source of growth and is important for analysing the economic performance of a production unit. We define productivity change as output change relative to input change in real terms. If more output is possible from a certain set of inputs or if less input is needed for a certain set of outputs in a firm then we say that there is a scope for improvement in capacity utilisation at the firm level. Better utilisation of capacity means use of inputs in more efficient manner. Capacity utilisation rate increases when firms move closer to the best practice production frontier, while technical progress shifts the frontier outward1. Clearly, factor productivity of a firm goes up due to technological progress and improvement in capacity utilisation. In a region with more efficient industries, productivity grows more rapidly and ultimately output expands at a faster rate raising its share of value added further. This paper carries out empirical estimates of productivity, capacity utilisation and technical change in manufacturing firms across the major industrial states in India with firm level data from Annual Survey of Industries (ASI) during the period 1998-2010 by applying stochastic frontier approach.

The essential sources of productivity growth are technological progress and the accumulation of capital, both tangible and non-tangible. Accelerating technological progress with exogenously given population growth is the crux of Robert Solow’s celebrated neoclassical model of economic growth. In Solow (2000) growth theory, population growth is exogenous, but the capital stock and the capital labour ratio are endogenous in responding to technological change. The endogenous nature of the growth of capital stock implies that the equilibrium growth rate of labour productivity is uniquely determined by the rate of technological change. In Solow’s production function, technological progress is labour augmented. If the production function exhibits constant returns to scale and both labour and capital are paid their values of marginal product, then output growth is determined by the growth rates of capital and labour inputs plus the rate of technological progress. If the rate of growth of capital input converges

In the neo-classical approach, technical progress is the ‘residual’ in its contribution to output growth after accounting for the contributions made by different inputs. In Solow residual, technical change is the unique source of total factor productivity growth. This measure, however, might not be a proper estimate of technical change in the presence of non-constant returns to scale and market power (Hall, 1988). 1

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to the rate of output growth, under Solow’s assumptions in the long run, then productivity growth converges towards the rate of technological change implying that the equilibrium growth rate of labour productivity is uniquely determined by the rate of technological change. Solow’s theory, however, fails to locate the driving forces of productivity growth. The objective of this paper is to estimate the sources of productivity growth in the Indian manufacturing industry. We have not followed the neoclassical tradition as such in estimating productivity2, but stochastic production frontier model developed in Battese and Coelli (1992) has been employed in a panel data frame under the assumption that efficiency is time invariant. A production function locates the maximum output that can be produced from a specified set of inputs, given the existing technology available to the producing units. In estimating total factor productivity in neoclassical frame, producers are assumed to operate exactly on the production function, implying that they able to utilise capacity fully in maximising output. In many cases, however, producers are likely to produce not on but inside the production frontier in output space implying that they cannot utilise production capacity fully. The deviation of actual output from the optimal one, for a given input combination, is conventionally known as inefficiency (Farrell, 1957) that may cause because of some unforeseen exogenous shocks experienced by the producing units while conducting the production process. We have used this concept of inefficiency as a measure of underutilisation of capacity. Farrell (1957) was the first to estimate efficiency empirically with US cross section data on agriculture and decomposed it into technical and allocative parts using linear programming techniques. Farrell’s work led to the development of two principal methods to compute efficiency scores: stochastic frontiers, based on econometric methods, and data envelopment analysis, relying on mathematical programming.

Aigner and Chu (1968) first used deterministic production frontier of Cobb-Douglas type and then Aigner, Lovell and Schmidt (1977), Meeusen and van den Broeck (1977) introduced stochastic production frontier model to estimate productive efficiency developed in Farrell (1957). Since then a stream of research has produced a number of innovations in specification

2

The empirical estimation of production functions begins around 1928 with the papers of Cobb and Douglas (1928). However, until the 1950s, production functions were largely used as devices for studying the functional distribution of income between labour and capital at the macroeconomic level, a notable example is the celebrated contribution of Arrow et al., (1961).

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and estimation of their model3. Stochastic frontiers accommodate statistical noise in the dependent variable by introducing a residual, while typically treating efficiency as a random parameter. The early empirical studies in the literature used cross-section data assuming inefficiency to be time independent. In panel data model proposed by Pitt and Lee (1981), while analysing the technical efficiency of Indonesian weaving industry, efficiency effects were considered to be time-invariant as well. Battese and Coelli’s (1992) study on the paddy farmers in India had used the random effect panel data model in estimating stochastic production frontier with time varying efficiency. Battese and Coelli (1995) extended their model by incorporating the estimation of the parameters of the factors believed to have influence on the technical efficiency levels of producers. This model simultaneously estimates the parameters of the stochastic frontier and the determinants of efficiency.

The stochastic production model does not necessarily require a behavioural assumption of cost minimisation or profit maximisation under competitive conditions (Nikaido, 2004) and other restrictive assumptions needed by the growth accounting based on neo-classical approach. The panel data model does not require some strong distributional assumptions that are needed for using cross section data (Kumbhakar and Lovell, 2000). In this framework it is possible to decompose the random error into the white noise and inefficiency components (both firm and time specific). This approach allows each cross section unit to have different levels of efficiency in different period.

On the basis of the stochastic frontier model, this study looks into the within group component of productivity growth as developed in Baldwin et al. (2012)4. The within group component captures the effect of capital and other input deepening, technological progress, scale economies, and input utilisation at the unit level. Labour productivity, measured as output per man hour worked, although more popular, is a gross indicator for capturing the enhanced welfare from growth5. Capital productivity, measured as output per unit of capital, on the other hand, reflects the efficiency with which capital stock is used. Both the measures, however, capture partial productivity of a firm. A more realistic representation of the productive

3

See Greene (2002) for details of the evolution of the stochastic frontier model. Baldwin et al. (2012) developed a model that can be used to show the extent to which the total growth in productivity comes from different underlying sources. 4

5

Labour productivity (GDP per capita) is popularly used as an indicator for growth.

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performance needs multiple inputs together to estimate what is known as multi factor productivity (MFP). While the most widely used MFP model (KLEMS-Y model) relates gross output to capital (K), labor (L), energy (E), materials (M) and services (S), this paper relates value added to capital (K) and labor (L).

The rest of the paper is organised as follows. Section 2 describes the data and construction of variables on which the empirical exercise has been carried out. Section 3 deals with analytical description of the econometric model used in this study. We have focussed mainly on some features of stochastic frontier function. Empirical findings have been analysed in section 4. Section 5 concludes.

1. The data This study utilises factory level (unit level) data from the Annual Survey of Industry (ASI) published by the ministry of statistics and programme implementation of government of India, the main data source of manufacturing industries under the registered sector in India. Panel data series of relevant variables described below for total manufacturing have been constructed from the factory level information with state as a cross section unit over 1998-2010. But we should mention three major problems inherited in the ASI data and one has to bear them while using this data base. First, industrial units are classified here on the basis of National Industrial Classification (NIC) of economic activities. The NIC has, however, been changing from time to time and it creates a serious problem of comparability of the factory units from one NIC to another at five-digit level of disaggregation6. Second, the frequent changes in sampling design and the coverage of factory units under the census sector lead to a deviation of the number of factory units reported in ASI from the number of factories actually operated7. Third, although

6

NIC70 was used from 1973-74, but it was replaced by NIC87 in 1989-90, was replaced further by NIC98 in the year 1998-99, by NIC04 in 2004-05 and finally by NIC08 IN 2008-09. Although concordance tables are provided by the CSO to combine different data series based on different NIC for constructing continuous time series, one can combine the data only at the two-digit or three-digit or at most four-digit level of disaggregation. 7

Initially, all units with 50 or more workers operating with power, and units having 100 or more workers operating without power were covered under the census sector. In 1987-88, the definition of the census sector was revised

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all firms registered under sections 2m(i) and 2m(ii) of the Factories Act 1948 are supposed to supply relevant information mentioned in the ASI schedule, many of them have failed to supply it regularly.

Gross value added in real terms is used as output variable. Among the input variables, gross value of plant and machinery is used as capital input. In ASI, it includes the book value of newly installed plants and machinery and the approximate value of rented in plants and machinery without adjusting depreciation. Two distinct types of labour inputs, namely, workers and non-workers are used in estimating the frontier. The numbers of workers and of employees are recorded separately in the ASI. In annexure IV of the ASI, workers are defined to include all persons engaged directly or indirectly in the production process. Employees, on the other hand, include all workers defined above and other persons engaged in supervisory and managerial activities. We may treat workers as less skilled labour compared to non-workers. Figures for non-workers are obtained by subtracting the number of workers from total employees engaged in a factory. As the level of efficiency of a producing unit depends on the qualitative character of labour as well as other factors, we have distinguished these two types of labour and examined their relative contribution to efficiency and productivity of the manufacturing units.

2. Econometric Model In a world without error or inefficiency, the production frontier of the ith producing unit in time t can be specified as yit  f xit ,  

(1)

Stochastic frontier analysis assumes that each firm potentially produces less than it might due to a degree of inefficiency. Specifically, yit  f xit ,   it

(2)

to include the units having 100 or more workers irrespective of their operation with or without power in to this sector. It was further modified in 1997-98 and the criterion for a unit to be regarded as a part of the census sector became that it should have a minimum number of workers of 200, working with or without power. Also, all public sector undertakings (PSUs), irrespective of the number of employees, were included in the census sector. In 200405, again a new sampling design was adopted and units with 100 or more workers were reconsidered as part of the census sector. Finally, the definition of the census sector was changed in 2007-08 and, additionally, joint sector firms were included in the census sector without considering the number of their employees.

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Here, ξit is the level of efficiency lying in the interval (0; 1) for firm i in time t. If ξit = 1, the firm could produce the optimal output with the technology embodied in the production function f(xit, β). When ξit < 1, the firm could not utilise the inputs xit efficiently given the technology embodied in the production function f(xit, β). As output is assumed to be strictly positive, the degree of technical efficiency is assumed to be strictly positive. A major drawback of the deterministic frontier model as specified in (2) is that the entire deviation of observed output from the maximum output is attributed to inefficiency. Such a specification does not incorporate random shocks such as structural adjustments, measurement errors and other conditions that are not under the control of a producer. Any particular firm faces its own production frontier which should be randomly placed by a collection of stochastic elements and thus stochastic components might enter into the model. When output is assumed to be subject to random shocks, the frontier function will be yit  f xit ,   it exp vit 

(3)

Taking log on both sides ln yit  ln f xit ,    ln  it  vit

(4)

Assuming k inputs, after incorporating time variable, the production function in log-linear form will be

ln yit   0    j ln x jit  t   it

(5)

j

Here, yit and xit are the gross value of output and a vector of inputs respectively of farm i in time t and βj’s are unknown parameters to be estimated, and

 it  vit  uit , uit   ln  it

.

The non-stochastic part of the right hand is the deterministic kernel or frontier, while the stochastic component includes both the effects of exogenous shocks and productive inefficiency. The Cobb-Douglas function is attractive for its simple log-linear form. The inclusion of time variable allows for shifting of the frontier over time that measures technical change. The stochastic frontier model has two types of random errors: εit = vit -uit, vit is the white noise





error term and assumed to be independently and identically distributed (i.i.d) as N 0,  v2 , and uit is asymmetric non-negative random variable distributed independently and identically as



N  ,  u2



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and is truncated at zero from below. The former captures the idiosyncratic

heterogeneity among firms as well as the variation in output that results from random factors that are beyond the control of the producing unit, while the latter takes into account productive inefficiency in the form of time-varying panel-level effect. Estimates of εit are easily obtained from the residuals of (5). However, this is a composite estimate of (vit - uit) and we need to estimate uit from it. The conditional mean of uit is

E u it  it    it*

where  it*

    it*      f   *    *     it*     1  F     *  

(6)

 v2   it  u2  u2 v2 2 2 2 2 ,    v   u and  *  ; f() and F() represent the standard  2 2

normal density and cumulative density functions, respectively8. Substituting the value of  it* , one can obtain the JLMS estimator of inefficiency9:

   it      f           it     E u it  it                  it  1  F          



(6*)

where

u v

In Battese and Coelli (1992 and 1995)  

 u2 is used in place of λ.  u2   v2

 and  are obtained from the MLE estimates of the variance parameters and the residuals. Both the efficiency level of each manufacturing industry and the distribution of efficiency 8

See Battese and Coelli (1988) for its derivation.

Jondrow, Lovell, Materov, and Schmidt (1982), first demonstrated the conditional distribution of u given ε and since then the estimate is known as the JLMS estimate. For derivation of it, see Kumbhakar and Lovell (2000). 9

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levels among all manufacturing industries in an economy influence mean efficiency of manufacturing in that economy. Higher efficiency at the industry-level combined with a higher degree of homogeneity in efficiency (measured by  u2 ) among industries yield higher level of mean efficiency. Equation (6) or (6*) yields the point estimate of the conditional mean of uit given εit. Given the distributional assumptions of uit and vit, the estimates of farm specific inefficiency can be calculated from the conditional distribution of uit given εit in the form of the ratio of observed output to the maximum potential output:

TEit 

Yit  exp  uˆ it  Yˆ

(7)

it

Technical inefficiency as defined in equation (7) by following Farrell (1957) has been used in this study as a measure of underutilisation of productive capacity. When the product and factor markets are competitive and the aggregate production function is characterised by constant returns to scale, aggregate multifactor productivity growth can be expressed as the difference between labour productivity growth and the effect of capital and other-input deepening: MFPg  d ln L1 p   k d ln(

L K )   L d ln( 2 ) 2 L1 L1

(8)

Here βk and βL2 represent the shares of capital and skilled labour inputs in nominal gross value added, averaged over the period.

3. Empirical results 4.1 Structural coefficients of manufacturing by states The regional dispersion of manufacturing growth in the factory sector has been clear in India. Table 1 displays some important structural ratios in explaining the regional dimensions of productivity growth in the manufacturing sector. In terms of capital-labour ratio, Gujarat was at the top, followed by Uttar Pradesh, Himachal Pradesh, Orissa, Madhya Pradesh and Karnataka. Capital intensity in registered manufacturing increased dramatically in Orissa during 1998-2010. The states including Jharkhand, Andhra Pradesh, Chhattisgarh, Uttaranchal and West Bengal also experienced a marked increase in capital labour ratio during this period. 9|Page

In Maharashtra, another leading industrial state of the country, however, capital labour ratio was low throughout the period. On the other hand, capital intensity in registered manufacturing declined in Uttar Pradesh, Karnataka, and Madhya Pradesh along with some other states during the same period.

Table 1 Changes in capital labour ratio and worker-employee ratio: 1998-2010 Capital labour Share of worker to ratio total employee (%) 1998 2010 1998 2010 Andhra Pradesh 9.7 14.6 82 79 Assam 7.4 9.0 84 85 Bihar 5.3 7.9 82 86 Chhattisgarh 6.2 12.8 75 75 Delhi 3.1 2.8 69 65 Gujarat 18.2 18.0 74 77 Haryana 5.7 7.4 73 78 Himachal Pradesh 16.4 18.1 76 77 Jammu and Kashmir 2.2 8.8 77 80 Jharkhand 9.6 19.1 75 68 Karnataka 12.7 10.6 74 78 Kerala 5.7 5.3 81 86 Madhya Pradesh 14.7 13.2 70 75 Maharashtra 6.4 8.5 58 71 Orissa 15.4 33.7 67 81 Punjab 6.8 7.5 79 79 Rajasthan 10.6 12.0 74 78 Tamil Nadu 8.0 8.0 81 82 Uttar Pradesh 16.5 8.6 73 77 Uttaranchal 8.4 12.5 79 81 West Bengal 2.9 9.1 80 81 All India 9.1 11.8 75 78 Source: Author’s calculation with firm level data from Annual Survey of Industries, Central Statistical Organisation, Government of India.

The composition of workforce engaged in producing units in the ASI sector has been dominated highly by the ordinary workers (Table 1). About three fourth of the total workers in registered manufacturing in India were ordinary workers mostly of them were unskilled in 1998 and the proportion increased to 78 per cent in 2010. In Assam, Andhra Pradesh, Bihar, Kerala, Tamil Nadu and West Bengal the share of ordinary workers was 80 per cent and above in 1998.

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The proportional share of unskilled to total workers varied between 65 per cent in Delhi and 86 per cent in Kerala and Bihar in 2010.

3.2 Regional variation in manufacturing growth Table 2 displays growth rates of number of factories, output and employment of labour and fixed capital in the registered manufacturing sector across different states in India over the period 1998-2010. Number of factory units increased at highly uneven rates across different regions of the country. Factory units grew at less than 4 percent rate at the national level. While Himachal Pradesh and Uttaranchal experienced significantly higher rate of expansion in manufacturing units, the industrially advanced states of Gujarat and Maharashtra along with other states like West Bengal exhibited a marginal rate of growth during this period. Output growth in registered manufacturing was spectacular during the past decade. Growth rate was more than 10 percent in most of the states with the highest rate in Uttaranchal. But, the growth rate was below the national average in some industrial states including Maharashtra, West Bengal and Tamil Nadu.

A positive correlation has been observed between the growth rates of output and fixed capital, but the correlation between output growth and employment growth of either type of worker was not significant (Table 2). Fixed capital in real terms grew at the highest rate in Uttaranchal and at the lowest rate in Kerala displaying a wide regional variation of it. Growth rates of employment were 5 percent and 3.8 percent for unskilled and skilled workers respectively at the all India level. Employment of unskilled labour increased at below 5 percent in Gujarat and below 3 percent in Maharashtra. The growth rate of employment of skilled workers, however, was significantly less than the rate for unskilled workers everywhere in the country. Employment growth was even negative in West Bengal and Jharkhand. West Bengal achieved 11 percent growth of real output with negative employment growth of either type of worker.

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Table 2 Growth rates of factory units, output, capital and labour: 1998-2010 States Andhra Pradesh Assam Bihar Chattishgadh Delhi Gujarat Haryana Himachal Pradesh Jammu and Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh Uttaranchal West Bengal All india

Number of factories 3.7* 4.8* 3.4* 5.0* -0.8 1.6*** 1.8* 12.7* 7.3* 3.6* 2.7* 2.7* 1.3** 1.6*** 3.0* 4.8* 3.5* 3.6* 1.9* 12.1* 1.3** 3.9*

Real output 11.2* 12.0* 13.0* 14.3* 4.2* 12.6* 10.7* 19.8* 21.6* 8.7* 13.0* 9.6* 5.9* 9.7* 14.3* 8.7* 10.0* 10.6* 9.7* 27.2* 11.0* 12.3*

Real fixed capital 10.1* 7.6* 5.7* 10.6* 1.7** 7.1* 7.6* 17.4* 20.3* 5.2* 6.9* 2.5* 3.6** 7.0* 16.7* 7.7* 5.6* 8.0* 1.5 25.2* 7.3* 8.8*

Ordinary worker

Skilled worker

2.5* 3.4* 3.9* 6.1* 0.1 4.8* 6.4* 13.3* 9.1* -2.1* 4.9* 2.8* 1.1 2.9* 7.1* 5.9* 5.8* 5.5* 3.8* 18.9* -0.3 5.0*

3.8* 2.5* 0.7 4.8* -0.3 3.2* 4.6** 12.6* 6.8* 0.1* 4.8* 0.5 0.3 1.1 2.9*** 5.2* 4.2* 5.5* 1.8** 16.1* -1.4** 3.8*

Note: Growth rates are calculated by estimating log linear trend in percentage form. The values of fixed capital and output are in real terms (at 1993-94 prices). * significant at 1% level, **significant at 5 % level, ***significant at 10% level, the rest are statistically insignificant. Source: As for Table 1

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4.3 Maximum likelihood estimates of Cobb-Douglas production frontier

In this study, the frontier function specified in equation (5) has been estimated by taking yit as gross value added by firms in region i in time t; xijt stands for input j (labour and capital) used in industries in region i at time t. We have 273 observations distributed in 21 regions over 13 years. Table 3 provides the maximum likelihood estimates of the Cobb Douglas production frontier as specified in equation (5) by considering efficiency component to be time invariant. The regression coefficients are statistically significant at 5 per cent level. The estimated output elasticity of capital was significantly higher than that of labour of either type. Thus the role of capital has been more important in output growth in the manufacturing sector. However, ordinary workers contributed to value added at a higher rate as compared to other type of employees. The sum of the elasticities, measuring returns to scale, equals unity implying the presence of constant returns to scale. The coefficient associated with the time variables t is nonnegative and statistically significant suggesting the presence of technical progress during the estimation period.

Table 3 Maximum Likelihood estimate of Cobb Douglas production function Estimated Coefficients β0 -81.90 β1 0.32 β2 0.20 β3 0.53 η 0.04 2 σ 0.11 γ 0.56 2 σu 0.06 σ2v 0.05 2 W χ (4) = 1689.49 2 Prob > χ = 0.00 Log likelihood = .70 Parameters

Source: As for Table 1

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Z statistic -8.69 2.3 1.81 9 8.32

Prob.>z 0 0.021 0.071 0 0

In addition to the regression coefficients, the Table 3 reports estimates for the parameters σ2u, σ2v  2   v2   u2 ,  

 u2 . Various tests of hypotheses of the parameters in the frontier  u2   v2

function can be performed using the generalised likelihood ratio-test statistic, defined by λ = 2 [L(H0)-L(H1)], where L(H0) is the log-likelihood value of a restricted frontier model, as specified by a null hypothesis, H0 ; and L(H1) is the log-likelihood value of the general frontier model under the alternative hypothesis, H1 . This test statistic has approximately a chi-square distribution with degrees of freedom equal to the difference between the parameters involved in the null and alternative hypotheses. The Wald chi square statistic, being significant at the 1 per cent level, confirms that the time invariant specification of inefficiency is the right choice for analysing efficiency performance of the registered sector. The γ is the variance-ratio parameter, which is also important in determining whether a stochastic production frontier is a superior measure to the traditional production function. The value of γ parameter significantly not different from zero means there is no inefficiency and in this case traditional production function will be the best fitted.

4.3 Sources of productivity growth and capacity utilisation

Output growth in the registered manufacturing sector was spectacular during the past decade (Table 2). But, labour productivity grew at the rate of 6 per cent and the growth rate was highly uneven across the major states during 1998-2010 (Table 4). Labour productivity growth varied from 3.3 in Haryana per cent to 11.8 per cent in Uttaranchal. In few regions the rate was not statistically significant. As stated above, MFP growth is obtained by subtracting the coefficients of capital deepening and skill deepening from productivity growth. The MFP growth rate in manufacturing industries in India was 4.2 per cent during this period. It was the highest in Jammu and Kashmir and the lowest in Assam (after ignoring insignificant productivity growth in Delhi and Punjab). In industrially advanced states of Gujarat and Maharashtra the MFP growth rate was moderate. The rate of capacity utilisation, estimated on the basis of the production frontier, was at around 67 per cent at the national level. The utilisation rate was the highest in Himachal Pradesh followed by the mineral rich state of Jharkhand. The rate of capacity utilisation was very poor in West Bengal. 14 | P a g e

Table 4 Sources of productivity growth and capacity utilisation: 1998-2010 States Andhra Pradesh Assam Bihar Chhattisgarh Delhi Gujarat Haryana Himachal Pradesh Jammu and Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh Uttaranchal West Bengal All India

Labour productivity growth 9.3* 4.3* 3.2 6.9* 1.4 5.3* 3.3* 9.4* 16.2* 8.7* 6.1* -0.2 5.2* 7.1* 8.2* 1.4 3.4* 3.9* 3.4* 11.8* 7.1* 6.0*

Capital deepening

Skill deepening

MFP growth

Capacity utilisation

4.0* 2.2* 1.0* 2.4* 0.8* 1.2* 0.6* 2.2* 5.9* 3.9* 1.1 -0.2 1.3* 2.1* 5.1* 0.9*** -0.1 1.3* -1.2 3.3* 4.0* 2.0*

0.3 -0.2** -0.6* -0.3 -0.1 -0.3* -0.4 -0.1 -0.5* 0.4** 0.0 -0.5* -0.2 -0.4*** -0.8* -0.2* -0.3* 0.0 -0.4* -0.6 -0.2** -0.2*

5.0 2.2 2.8 4.8 0.6 4.4 3.0 7.3 10.8 4.4 5.0 0.5 4.0 5.3 4.0 0.6 3.8 2.6 5.0 9.0 3.3 4.2

49.4 61.8 50.6 86.0 87.2 64.5 72.2 93.1 71.6 92.0 61.1 54.6 66.2 81.0 55.8 58.3 63.0 51.4 57.0 86.8 47.7 67.2

Note: Growth rates are calculated by estimating log linear trend in percentage form. Capacity utilisation scores have been predicted from the estimated frontier function. * significant at 1% level, ** significant in 5% level, *** significant in 10% level, the rest in first three columns are insignificant. Source: As for Table 1

4. Conclusions

This paper carries out empirical estimates of productivity, capacity utilisation and technical change in manufacturing firms across the major industrial states in India during the period 1998-2010 by applying stochastic frontier approach in a panel data frame. We define productivity change as output change relative to input change in real terms. If more output is 15 | P a g e

possible from a certain set of inputs or if less input is needed for a certain set of outputs in a firm then we say that the rate of capacity utilisation of the firm improves. Growth rates of output and labour productivity are calculated by employing log linear trend across different states over the past decade. In this paper we have examined the sources of productivity growth in manufacturing industries by taking regional dimension into account. The regional dispersion of manufacturing growth in the factory sector has been clear in India. In terms of capital-labour ratio, Gujarat was at the top. In Maharashtra, another leading industrial state of the country, however, capital labour ratio was low throughout the period. The composition of workforce engaged in producing units in the ASI sector has been dominated highly by the ordinary workers. Gross value added in real terms grew at a significantly higher rate than the rate of growth of conventionally measured productivity. The pattern of growth of different inputs suggests, although grossly, that the structural change in Indian manufacturing industries took place in favour of capital. The estimated output elasticity of capital was significantly higher than that of labour of either type. However, ordinary workers contributed to value added at a higher rate as compared to other type of employees. The estimated results suggest a significant role of capital and technology in the process of manufacturing growth in India. The sum of the elasticities, measuring returns to scale, equals unity implying the presence of constant returns to scale.

In this study, the experience of industrial growth across major states of the country highlights that MFP growth and the extent of capacity utilisation has been a significant part in explaining regional disparities in industrial growth. The regional variation in labour productivity growth was accompanied by the variation in MFP growth and capacity utilisation rate.

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References Aigner, D and S. Chu. (1968). On Estimating the Industrial Production Function, American Economic Review, 58, 826-39. Aigner, D, K. Lovell and P. Schmidt. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models, Journal of Econometrics, 6, 21–37. Arrow, K., H. Chenery, B. Minhas and R. Solow. (1961). Capital Labour Substitution and Economic Efficiency, Review of Economics and Statistics, 45, 225-47. Baldwin, J., W. Gu, and B. Yan. (2012). Export Growth, Capacity Utilization and Productivity Growth: Evidence from Canadian Manufacturing Plants, Economic Analysis Research Paper Series. Statistics Canada, Catalogue no. 11F0027M, no. 075 Battese, G. E. and T.J. Coelli.(1988). Prediction of Firm Level Technical Efficiencies With a Generalised Frontier Production Function and Panel Data, Journal of Econometrics, 38, 387399. -

(1992). Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India, Journal of Productivity Analysis, 3, 153–69. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data, Empirical Economics, 20, 325- 32.

Cobb, S. and P. Douglas. (1928). A Theory of Production, American Economic Review, 18, 139-65. Das, P. (2007), Economic Growth and Structural Break in India: Testing Unit Root Hypothesis, The Journal of income and wealth 29(2), 29-43 Farrell, M. J. (1957). The Measurement of Productive Efficiency, Journal of Royal Statistical Society, Series A, General, 120, 253-82. Greene, W. (2002). Alternative Panel Data Estimators for Stochastic Frontier Models, paper prepared for the conference on Current Developments in Productivity and Efficiency Measurement, University of Georgia, October 25-26, 2002. Hall, R. (1988). The Relation between Price and Marginal Cost in U.S. Industry, Journal of Political Economy, 96, 921–947. Jondrow, J., Lovell, C.A.K., Materov, I.S., and Schmidt, P. (1982). On the Estimation of Technical E_ciency in the Stochastic Frontier Production Function Model, Journal of Econometrics, 19 (2-3), 233-38

17 | P a g e

Kaldor, Nicholas (1981). The role of increasing returns, technical progress and cumulative causation in the theory of international trade and economic growth, Economie Appliquee, no.4, reprinted in Targetti and Thirlwall (1989). Kumbhakar, S. C. and C. Lovell. (2000). Stochastic Frontier Analysis, Cambridge University Press, Cambridge Mueller, J. (1974). On Sources of Measured Technical Efficiency: The Impact of Information, American Journal of Agricultural Economics, 56, 730-738 Nikaido, Y. (2004). Technical Efficiency of Small-Scale Industry - Application of Stochastic Production Frontier Model, Economic and Political Weekly, 7 February, 592-97. Pitt, M. and L. Lee (1981). The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry, Journal of Development Economics, 9, 43-64 Solow, R.M. (2000). Growth theory: An exposition, 2nd edition (Oxford, New York, Oxford University Press, 2000).

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