Decomposition of productivity growth in Indian metallic mining industry

Auro Kumar Sahoo1

Ph.D Scholar, School of Humanities, Social Sciences and Management,

Indian Institute of Technology BhubaneswarOdisha. Pin-751007.

E-Mail: aks11@iitbbs.ac.inMobile: +91-9853872991

Dukhabandhu SahooAssistant Professor

School of Humanities, Social Sciences and Management,Indian Institute of Technology Bhubaneswar

Odisha. Pin-751007.E-Mail: dsahoo@iitbbs.ac.in

Ph: 0674-257-6152

Naresh Chandra SahuAssistant Professor and Head

School of Humanities, Social Sciences and Management,Indian Institute of Technology Bhubaneswar

Odisha. Pin-751007.E-Mail: naresh@iitbbs.ac.in

Ph: 0674-257-6163

1 Correspondence Author

Decomposition of productivity growth in Indian metallic mining industry

Abstract:

The paper aims at decomposing the total factor productivity (TFP) growth of 31 Indian

metallic mining firms those are involved in mineral extraction since 1988 to 2014.

Productivity growth has been estimated by using stochastic production frontier (SPF)

technique based on translog production function. It is found that the annual average TFP

growth of metallic mining industry increased from 5.35 % during 1989-2004 to 11.70 %

during 2005-2014. The productivity growth in mining grows steadily since the mid of the

first decade of present century. Further, results of the decomposition of TFP growth into

technological progress (TP), technical efficiency change (TEC) and scale components (SC)

reveal that the major source of productivity growth was TP in initial years. However, the

leading source of TFP growth has changed from TP to TEC in recent periods. In view of this,

it could be suggested that metallic mining industry in India requires to focus on investment in

innovation and upgradation of existing technology to enhance productivity further.

Keywords: Total Factor productivity, Metallic mining, Panel data, Stochastic frontier analysis (SFA).

JEL Classification: C23, D24, L72

1. Introduction:

Metallic minerals are used as the basic input to the industrial sector of an economy.

Indian economy experienced a strong backward linkage of industrial sector with metallic

mining in terms of its huge demand for iron ore, manganese ore, bauxite, chromite etc.

Historical overview of Indian mineral sector reflects a paradigm shift in purview of the

adoption of National Mineral Policy (NMP) in 1993. The strict policy towards mining

operation has been liberalised in view of strong and vibrant requirement of minerals for

manufacturing and industry sector for economic growth of the country.

1

The production statistic reflects a tremendous growth in the production of metallic

minerals soon after the adoption of NMP in 1993. Trend of the total value of metallic

mineral produced in India has shifted from Rs. 2970 million in 1981 to a higher level of

Rs 16340 million and Rs. 39780 million in 1991 and 2001 respectively (Indian Mineral

Yearbook (IMY), 2013). Similarly, metallic mineral accounted for 7.87 % of total value

of mineral production in 1990-91, which rose up to 15 % in 2013-14 (IMY, 2013). The

upward trend in the mineral production after the adoption of economic reforms as well as

the adoption of NMP requires the attention of researcher for in-depth investigation.

An unprecedented upward shift in output growth of metallic mining is most likely

achieved through stimulation of investment in mining activities or adoption of advanced

technology through a liberal policy towards mining. However, the effect of shift in scale

of operation due to policy changes is likely to have an impact on the output growth.

Production and productivity growth are important aspect, which need careful attention

while discussing the unparalleled shift in output growth of metallic mining in context of

India.

Being an important part of mining industry, metallic mining supplies raw materials to

the industry sector uninterruptedly which enhances economic growth of the country

through production and productivity growth in industrial sector of economy. Against this

backdrop, the present study enquires the possibility of the output growth in context to

metallic mining in India. The possible source of output growth is analysed through total

factor productivity and its different sources. In other words, upward trend in output

growth has been analysed through sources such as technological progress, technical

efficiency change and scale of operation. The TFP growth of metallic mining industry has

been estimated for the period 1989 to 2014 based on the decomposition procedure of TFP

2

growth estimation. The lack of availability of data has restricted the period of study to

1988 onwards.

On productivity growth in mining sector, we can hardly find a comprehensive study

based on multi-factor productivity approach. The review of existing literature on

productivity growth in Indian mining industry reveals that no such specific study

available for metallic mining. However, metallic mining is important because of the close

forward linkages with the secondary sector of the economy. Further, previous studies on

partial productivity are not comprehensive and complete in respect of recent productivity

estimation procedure. As a result, literature on mining is in need of an inclusive work

based on multifactor productivity, accompanying recent approach of productivity

estimation, which has been focused on through this empirical analysis on Indian metallic

mining sector. The unique contribution of the present work lies in decomposition of total

factor productivity into different components using the time varying stochastic production

frontier approach for the metallic mining industry.

The rest of the paper is organised in the following manner. Section 2 gives a bird eye

view of the historical trend in metallic mining sector of India. In Section 3, review of

literatures on productive efficiency has been made relating to mining as well as the

industry sector. Section 4 discusses issues relating to the analytical framework such as

model specification, TFP decomposition techniques, functional specification, data

collection and variable construction. Section 5 analyses the empirical results and

highlights the findings; and the last section concludes the study with suitable policy

recommendations.

2. An Overview of Metallic Mining in India

India has experienced a noticeable growth in mineral production in terms of both quantity

and value. Production statistics of minerals reflect that 11 major metallic minerals are

3

produced in Indian subcontinent. The production of metallic minerals holds significant

position in terms of contribution to the world mineral market. India’s position in

production of metallic minerals is third in Chromite, fifth in Iron ore, sixth in Bauxite and

seventh in Manganese ore (IMY, 2013). It reflects the relative importance of metallic

mining being a subsector of mining industry in India. Similarly, metallic minerals account

for 15 % of total value of mineral production in India (IMY, 2013).

The composition of mineral basket produced in India is presented in Figure 1. Mining

industry in India is composed of minerals from the sub category fuel minerals, metallic

minerals, non-metallic minerals and minor minerals. In 1951, metallic minerals accounted

a share of 23.75 % of total value of mineral produced in India, which declined to 7.87%

and 6.52 % in 1999 and 2001 respectively. However, a steady increasing trend was

noticed soon after 2001, which placed the figure at 18.34% in 2011; whereas the figure of

total value of metallic minerals produced in India narrate a completely different picture.

Metallic minerals accounted a total value of Rs. 19 thousand crore in 1951 which moved

up to Rs. 1634 thousand crore in 1991 and further to a higher level of Rs. 46902 thousand

crore in 2011. It is clearly reflected in Figure 1 as given below.

Figure 1: Trend of mineral composition in India and role of metallic mining

1951 1961 1971 1981 1991 2001 20110%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fuel Minerals Metallic Minerals Non-metallic and Minor mineralsValue of metallic minerals

Prop

orti

onal

com

posi

tion

of

min

eral

s

Val

ue o

f pr

oduc

tion

(in

Rs.

'000

cro

re)

4

Source: Indian Mineral Industry at a glance 2011-12, Indian Bureau of Mines Nagpur

Mining sector is one of the most important sources of employment in India. A

comparative analysis of employment generation between mining sector and metallic

mining sub sector for the period 2002-03 to 2012-13 is presented in Figure 2. The average

daily employment in metallic mining sector is estimated to be 69268 persons during 2002-

03 which is 12.20 % of average daily employment in mining sector. During the period of

observation, proportional contribution of metallic mining to employment generation is

continuously moving upward except for the period 2012-13. The average daily

employment contribution is estimated at 14.67 % of daily employment in aggregate

mining during 2012-13 (Indian Bureau of Mines, 2012).

Figure 2: Trend of Employment Generation in Metallic Mining of India

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-130

100000

200000

300000

400000

500000

600000

0

2

4

6

8

10

12

14

16

18

Metallic minerals Total mining industryShare in average daily employment in mining

Ave

rage

Dai

ly E

mpl

oym

ent

Em

ploy

men

t sha

re o

f M

etal

lic m

iing

(%)

Source: Indian Mineral Industry at a glance 2011-12, Indian Bureau of Mines Nagpur

The above discussion emphasizes the role of metallic mining industry in Indian

economy. Metallic mining is contributing to the economy through employment generation

and supply of raw materials to other sectors of the economy through which it support to

the overall economic performance of the country. The unprecedented output growth after

NMP for metallic mining industry induced further investigation of its sources through a

productivity growth analysis. In order to overview previous works, literature pertaining to

5

productivity growth in mining industry have been reviewed, which are presented in the

following section.

3. Literature Review

Productivity growth in context to mineral resource extraction has been investigated in

different countries. All the existing literature are based on partial productivity estimation

focusing labour productivity growth. It is also observed that most of the studies focus on

finding productivity for specific mining. In Indian context, a few number of studies are

available relating to productivity growth in mining industry that are also presented in this

section.

Labour productivity is well evident of achieving output growth in context of different

mining. Role of labour productivity in achieving output growth and comparative

advantage in mineral production have been reflected in context to many mineral abundant

country (Darmstadter, 1999; Tilton and Landsberg, 1997; Aydin and Tilton, 2000; Garcia

et al., 2001; Tilton, 2001). The labour productivity relating to copper industry in United

States reveals that technological innovation is important for mineral endowed country to

get comparative advantage in production (Aydin and Tilton, 2000). Similarly, labour

productivity growth is found to be largely driven by technological change in Chile

(Garcia et al., 2001). However, quality of mineral deposit and operational factors are

found to be equally important in addition to technological factors and innovations to

enhance labour productivity in context of copper mining in Chile and Peru (Jara et al.,

2010). In productivity literature, many scholars attempt to investigate drivers of

productivity growth in relation to mineral resource. Technological factors, operational

structure and social determinants of productivity are reflected in the literature

(Darmstadter, 1999; Naples, 1998; Humphris, 1999).

6

Productivity estimation based on production frontier has an

advantage of decomposing it into different components. Solow (1957)

identified the technical change in a production frontier by

differentiating between the movement within a frontier and shift of

frontier. Further, efficiency change has been incorporated to

productivity change by considering the shift in frontier and movements

towards or away from it (Nishimizu and Page, 1982). In a study,

Lansbury and Mayes (1996) observed the production frontier shift over

the period of observation and found that the average level of

productivity would rise without necessarily having a fall in technical

inefficiency. Based on decomposition methodology, Kulshreshtha and

Parikh (2002) decomposed total factor productivity growth of coal

mining in India and found evidence of technical advances in surface

mining and efficiency improvement in underground mines as

contributing factor of TFP growth. However, output growth in case of

Australian mining industry is evident of being input driven rather than

productivity (Asafu-Adjaye and Mahadevan, 2003). In the context of

Indian mining sector, private mining firms are found to be extracting

more efficiently than public mining firm (Das, 2012).

After reviewing the existing studies on productivity growth in mining

sector, it is observed that most of the studies are based on partial

measure of productivity and focus on specific minerals. In order to fill

the gap, in this study an attempt has been made to undertake a

comprehensive study on Indian metallic mining, based on total factor

7

productivity. The details of the methodology adopted for the study is

given in the next section.

4. Analytical Framework

Productive efficiency and its measurement through segregation are first highlighted in the

work of Farrell (1957). Technical efficiency can be measured using either through input

oriented or output oriented procedure. The output oriented technical efficiency deals with

the ability of a production unit to maximise the output level with the given level of input

bundles. In contrast, input oriented technical efficiency is to attain a given output level

with the minimum possible input combination. However, allocative efficiency is the

ability of a production unit to optimise the combination of inputs and outputs considering

respective prices with the available technology. In this paper, we have followed the

method of Farrell (1957) and estimated the output oriented efficiency using stochastic

frontier technique. Based on the stochastic frontier models, total factor productivity

growth is measured as the summation of technical progress, technical efficiency change

and scale efficiency. In other words, TFP growth can be decompose into these

components.

4.1. TFP Decomposition

Potential output level in a production frontier using a vector of inputs can be written in a

production function framework;

y¿p=f ( x¿ , β , t ) …………(1)

where y¿p is the maximum feasible output to be produced by i th production unit over

tth time period. In this production process i = 1, 2, ....., N and t = 1, 2, ......., T. Thex¿ is a

vector of input which is used to produce along with the given technology represented by

the time trend ‘t’. Based on this production function, an actual output level with output

oriented technical inefficiency can be written as:

8

ln y¿=ln y¿p−u¿…………(2)

y¿in the above equation is actual output level produced by a firm with presence of

inefficiency u¿in the production process. The inefficiency is the logarithmic difference

between potential output level in the frontier and the actual output. Which can be

expressed as:

u¿=ln y¿p− ln y¿ ;u¿ N+¿ (μ , σu

2 )… …… …(3)¿

The inefficiency effect is a positive truncation of normal distribution which can be

modelled as a product of exponential function of time (Battese and Coelli, 1992; Greene,

1997). It can be written as:

u¿=ηt u i=ui [exp {−η ( t−T ) } ] …………(4)

In the above equation η is an unknown parameter dealing with the rate of change in

technical inefficiency, which is multiplied with the inefficiency effect (u) of i th farm in the

observation of last year. As a result, the technical efficiency in the last period is a

deterministic exponential function of earlier periods.

A farm wishing to operate at the frontier requires to enhance its output level by

u¿× 100 % with the same level of input. From eqn (2) technical efficiency can be

estimated as the ratio of actual output level to maximum feasible output level as:

exp (−u¿ )= y¿

y¿p …………(5)

In the above equation (2), a stochastic element can be introduced to capture the

stochastic error in production process and the model becomes

ln y¿= f ( x¿ , β , t )+v¿−u¿…… …… (6)

The time varying stochastic frontier technique can be used to decompose the total

factor productivity growth into different components such as technological progress,

technical efficiency change, allocative efficiency and scale efficiency. Following Aigner,

9

Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977), a stochastic

production function for the maximum feasible output level produced by ith firm in tth time

period could be written as:

y¿=f ( x¿ , β , t )exp ( v¿) exp (−u¿ ) …………(7)

In the above model, there is presence of two errors; v¿ is the symmetrically distributed

random error and u¿is the producer specific inefficiency error. The output oriented

inefficiency term is characterised to be non-negative in nature. Moreover, both v¿ and u¿

are independently and identically distributed (IID). But v¿ N (0 , σv2) and u¿ follows non-

negative one sided symmetric distribution. The x¿ is a vector of input variables, used for

the production of output. Further, the time trend index ‘t’ is used as a proxy for

technological change. The derivative of logarithmic of input component of equation (6)

with respect to time can be written as:

d ln f ( x¿ ,t )dt

=∂ ln f ( x¿ ,t )

∂ t+∑

j

∂ ln f (x¿ , t )∂ x j

d ( x j )dt

…………(8)

It can be observed from the above equation that change in frontier output is possible

through two ways. Firstly, by change in technological progress as explained by the first

term in right hand side of above equation. Secondly, input use can cause to change in

frontier output, which is explained by the second term in the right hand side of the

equation. In place of the second term in right hand side, output elasticity can be written

for jth input. So that equation (8) becomes

d ln f ( x¿ ,t )dt

=TP+∑j

ε j x j …………(9)

where, the output elasticity of input ‘j’ is ε j, which can be expressed as ε j=∂ ln f ( x¿ , t )

∂ x j.

By total differentiation of the logarithmic of output in equation (7) with respect to time,

the output growth ( y¿) becomes

10

y¿=d ln f (x¿ )

dt−

dudt

…………(10)

By substituting eqn (9), the above equation becomes

y¿=TP+∑j

ε j x j−¿ dudt

…………(11)¿

Above equation states that output growth is determined by technological progress,

changes in input use and change in inefficiency over time. Total factor productivity

growth can be stated as the residual part of output growth that is not due to input change,

which can be written as:

T F P= y−∑j

P j x j………… (12)

where share of input j’s cost in production is noted by P j . Further, following Kumbhakar

and Lovell (2000), above equation can be written by substituting equation (11) in equation

(12). By this we can write

T F P=TP−dudt

+∑j

( ε j−P j ) x j=TP−dudt

+( RTS−1 )∑j

λ j x j+∑j

( λ j−P j ) x j …………(13)

In the above equation, RTS refers to return to scale which can be estimated as

RTS=∑j

ε j and λ j=ε j

RTS . As it is observed from equation (13), TFP growth has been

decomposed into various components as explained above. The second and third components are

nothing but the technical efficiency change, and scale component respectively. Last component of

the equation is inefficiency in resource allocation termed as change in allocative inefficiency.

However, in the present study, the unavailability of price information for all inputs does not allow

to estimate allocative inefficiency. Following Kumbhakar and Lovell (2000), the study restrict to

first three components in equation (13).

4.2. Functional Specifications

Productive efficiency is measured through the production function technique. In the

present case, production function consists of three inputs and single output which has

11

been adopted for the analysis. The translog production function for the present analysis

can be written as

ln y¿=α 0+α l ln L¿+αk ln K ¿+αe ln E¿+12

β¿ (ln L¿)2+ 12

βkk (ln K¿)2+ 12

βee ( ln E¿ )2+ βlk (ln L¿) (ln K ¿)+βke ( ln K ¿) (ln E¿ )+β¿ (ln L¿) ( ln E¿ )+ βtl (ln L¿) t+β tk ( ln K ¿ )t +β te (ln E¿) t+αt t +12

β tt t2+ (v¿−u¿ )………… (14)

In the above equation, y¿ is the observed output level for ith firm at tth time period. L,

K and E are labour, capital and energy input of firm respectively. Translog specification of

production function has the advantage over Cobb-Douglas specification in allowing non-

neutral technological progress in the model. The above model becomes a Cobb-Douglas

specified production function, if all the β is equal to 0 in the model (β=0). Moreover, the

model will hold a neutral technological progress when reduced to Cobb-Douglas

specification. From the above equation, technological progress can be estimated by

differentiating the output with respect to time.

TP¿=∂ ln ( y¿ )

∂t=β tl (ln L¿)+β tk ( ln K¿ )+ βte ( ln E ¿)+α t t+β tt t ………… (15)

where, β tl , β tk and β te are coefficient of labour capital and energy respectively, which can

be obtained from equation (14).

In the similar fashion, elasticity of output with respect to j th input can be obtained by

differentiating the function with respect to each particular inputs.

ε j=∂ ln f ( x¿ , t )

∂ ln x j=∑

i ≠ jβ ji ln x i+ β jj ln x j+β tj t …………(16)

4.3. Data and Variable Description

The study is based on the data extracted from Prowess database of Centre for Monitoring

Indian Economy (CMIE). Prowess is an electronic database, which maintains firm level

information on identification of firm, ownership status, financial statement, raw material

use and output status of individual firm. Annual data on different indicators of mining firm

has been collected for the period of 1988-89 to 2014-15. All the mining firms are selected

at two digit level, following the National Industrial Classification (NIC) 2008. Prowess

12

data sheet provides data series on input and output relating variables. But it is required to

construct appropriate variables from the available data for the research work. In this

analysis, we have considered three input variables namely labour, energy and capital.

The output series of metallic minerals has been deflated by Wholesale Price Index

(WPI) series of metallic minerals to obtain the output series for the use of present analysis.

Labour input is measured through the wages and salary expenses reported by the metallic

mining firms for the production. Wages and salary expenditure requires to be deflated with

Consumer Price Index (CPI) of industrial worker. However, the data on CPI series is

available with base 1982=100 and base 2001=100. Since the data on CPI series with base

2004=100 is not available, wages and salary expanses are deflated with WPI data. Labour

input used in the study is in labour days, which is estimated by dividing total wages and

salary expenses by average daily wages. The average daily wages figure is estimated from

the average weekly wages figure by considering six working days in a week. Data on

average weekly wages is collected from various issues of “Statistics of Mines in India”2.

Energy variable has been constructed by deflating the power and fuel expenditure of

mining firms with the WPI series of fuel and power (2004=100), as supplied by the

Economic Advisor, Ministry of Commerce and Industry. Although each individual firms

are supplying figure relating to capital stock that are in historical prices, we have re-

estimated it in actual prices using Perpetual Inventory Method (PIM)3. Following

Srivastava (1996), the Gross Fixed Asset (GFA) values is revalued at replacement cost

with 2009-10 as the benchmark year. The estimated revaluation factor has been multiplied

with the GFA of benchmark year to convert it from historical cost to replacement cost.

5. Empirical Analysis

2 Published by Ministry of labour and employment , Government of India3 For more details see Srivastava (1996)

13

The results obtained through decomposition of TFP growth for metallic mining industry

has been analysed in this section based on the components, such as technological

progress, technical efficiency change and scale efficiency. It will not only provide

opportunity to understand the sources of productivity growth but also show the intensity

of different components over the period of study. As a result, it is more valuable in

respect of policy formulation for metallic mining. All the estimation in this study have

been undertaken using the programme FRONTIER 4.1 and statistical software STATA

13.1. The descriptive statistics of the variables employed for the metallic mining industry

is presented in Table 1 as given below.

Table 1: Descriptive Statistic of the Variables used in the Study

VariableMean Standard

DeviationMinimum Maximum

Output (Y) 7.0 0.9 3.9 9.2Labour (L) 5.4 1.0 2.5 7.2Energy (E) 5.4 1.2 1.1 8.3Capital (K) 6.8 0.8 3.8 9.2Total Observation

582

Note: (1) All variables are in logarithmic formSource: Author’s estimation

Productivity estimation requires validation of modelling the inefficiency effect and

technological change for reliable result. Beside this, choice of appropriate functional form

of the production function is necessary before estimation of productivity growth and

decomposition to avoid spurious results. In order to avoid these problems and to choose

an appropriate functional form as well as proper modelling of inefficiency effect, seven

basic models and one unrestricted model have been estimated. Previous literature on

micro analysis of productivity is well evident of the estimation of these models for

validation of modelling (Battese and Coelli, 1992; Kumar, 2002; Madheswaran et al.,

2007; Mandal and Madheswaran, 2012). The details on specification of all the estimated

models are presented in Table 2.

14

Table 2: Specification of Models for Testing Validity of Modelling Inefficiency

Model Specification1.0 Unrestricted model based on translog production function with all parameters

1.1Time varying inefficiency following truncated normal distribution and based on Cobb-Douglas production function

1.2Time varying inefficiency based on translog production function with absence of technical change

1.3Time varying inefficiency based on translog production function with Hicks-neutral technical change

1.4Translog production function with presence of zero inefficiency in production process

1.5Translog production function with inefficiency term following a half normal distribution

1.6 Translog production function with inefficiency term is time invariant

1.7Translog production function with inefficiency term following a half normal distribution but time invariant

Source: Compiled by Author

All the alternative specifications, as explained in Table 2, have been estimated and

reported in Table 3. The following table reflects the estimated value of the coefficient

captured in the model and their respective t-statistic in the parenthesis. In addition, four

additional parameters relating to the restrictions applied for modelling the inefficiency

effect have been reported in the table. The composed variance consisting of stochastic and

inefficiency error is expressed with σ2. In the total variance, proportion of variance due to

inefficiency is noted with γ, which varies between zero and one. Similarly, the

inefficiency term, which holds a positive value, can be modelled to follow a half normal

or truncated normal distribution. It is captured through an additional parameter μ. The

inefficiency term, following half normal distribution, is obtained by restricting μ to be zero.

Inefficiency can be captured through setting it as constant or varying over time, using an

additional parameter η.

Table 3: Panel Frontier Models with Different Specification for Metallic Mining industry

VariableModel

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7

15

Constant 0.681(0.68)

2.22***

(7.04)3.080**

(2.03)-1.605(-1.60)

2.669***

(2.81)-2.554**

(-2.20)6.981***

(5.81)7.362***

(4.08)

ln L 1.652***

(4.37)0.15***

(3.29)1.523***

(4.38)1.755***

(5.13)1.685***

(4.16)1.998***

(5.69)1.340***

(3.90)1.214***

(3.52)

ln E 0.136(0.49)

0.22***

(7.86)0.304(1.26)

0.091(0.38)

0.414(1.57)

-0.233**

(-2.38)0.477*

(1.64)0.724**

(2.51)

ln K 0.013(0.02)

0.44***

(8.70)-0.819(-1.56)

0.377(0.88)

-1.387**

(-2.49)1.012*

(1.88)-1.530***

(-3.29)-1.838***

(-2.76)

(ln L)2 0.134(1.41)

0.053(0.56)

0.079(0.88)

0.083(0.80)

0.118***

(3.30)0.204**

(2.13)0.227**

(2.39)

(ln E)2 -0.032(-0.60)

0.113**

(2.08)0.176***

(3.94)0.220***

(3.75)0.062**

(1.95)-0.098*

(-1.69)-0.058(-0.97)

(ln K)2 0.336**

(2.31)0.542***

(3.87)0.343***

(2.71)0.615***

(3.76)0.132***

(2.95)0.655***

(4.78)0.705***

(4.54)

(lnL)(lnE) 0.080(1.47)

0.023(0.44)

-0.005(-0.10)

-0.043(-0.73)

0.009**

(2.17)0.124**

(2.22)0.084(1.41)

(lnL)(lnK) -0.409***

(-4.51)-0.285***

(-3.09)-0.288***

(-3.46)-0.248**

(-2.37)-0.374***

(-4.09)-0.509***

(-5.79)-0.470***

(-5.25)

(lnE)(lnK) 0.006(0.10)

-0.133***

(-2.64)-0.129**

(-2.54)-0.167***

(-2.58)0.027**

(2.45)-0.001(-0.02)

-0.043(-0.70)

T -0.018(-0.59)

0.03***

(5.58)0.042***

(4.04)0.089***

(2.61)-0.045*

(-1.62)-0.033(-1.18)

-0.025(-0.78)

t2 -0.004***

(-5.88)-0.0007(-0.82)

-0.005***

(-5.91)-0.002***

(-2.57)-0.005***

(-7.75)-0.005***

(-8.46)

(lnL)t 0.001(0.33)

0.010*

(1.79)-0.003(-0.61)

0.017***

(3.45)0.014***

(2.93)

(lnE)t -0.017***

(-3.96)-0.012**

(-2.34)-0.013***

(-2.94)-0.025***

(-5.55)-0.024***

(-5.58)

(lnK)t 0.027***

(4.21)-0.002(-0.38)

0.030***

(4.31)0.020***

(3.06)0.021***

(3.06)

σ2 0.45***

(10.65)0.59***

(13.57)0.60***

(3.01)0.50***

(11.37)0.23

3.14***

(2.77)0.40***

(8.78)1.16***

(3.26)

γ 0.76***

(26.37)0.80***

(38.64)0.79***

(11.47)0.77***

(26.79)0.96***

(73.45)0.72***

(23.63)0.90***

(28.15)

μ 1.18***

(5.89)1.38***

(7.03)0.95***

(3.80)1.25***

(6.62)0.00

1.08***

(7.05)0.00

η -0.04***

(-5.47)-0.07***

(7.57)-0.04***

(-3.67)-0.08***

(-7.66)-0.06***

(-4.75)0.00 0.00

Log-likelihood

-236.37 -262.75 -268.96 -244.24 -249.26 -235.41 -244.51 -249.26

Note: (1) *, ** and *** represents significant at 10 %, 5% and 1% level (2) The values in parenthesis is respective t statistic

The selection of appropriate model from all alternative specification is done through

the likelihood-ratio (LR) test. In Table 4, the test result for different null hypothesis has

been presented. The test statistic for LR test isλ=−2 [ L ( H 0 )−L ( H 1 ) ], where first and

second terms inside the square bracket are values of log-likelihood function under

16

specification of null and alternative hypothesis respectively. When null hypothesis is true,

λ has approximately a Chi-square or a mixed Chi-square distribution with degrees of

freedom equal to the number of restrictions. The LR statistic for testing the hypothesis

concerning inefficiency effect and translog production function reveals that stochastic

translog production function with inefficiency term following a half normal distribution is

the best fit model. In the selected model, all the coefficients are highly significant which

is very useful for further estimation of productivity growth. Testing of hypothesis for the

model selection is presented in Table 10, which shows that none of the model except

Model 1.5 can be selected based on LR test. Moreover, it is found to have highest number

of significant coefficients as compared to other estimated models. Based on the

specification of frontier production function as well as the statistical test on estimated

parameters, Model 1.5 is selected as the best fit model. The details of hypothesis testing

are provided in Table 4 as given below.

Table 4: Test of Hypothesis for Modelling Inefficiency and Appropriate Functional Form in Metallic Mining Industry

Models Null Hypothesis (H0) Degrees of

freedom

Test statistics

(λ)

χ0 . 012 χ0 . 05

2 Decision

1.1 All β s=0 10 52.76 23.20 18.30 Reject H0

1.2 α t=βtt=β tL=β tE=β tK=0 5 65.18 15.08 11.07 Reject H0

1.3 β tL=β tE=β tK=0 3 15.72 11.34 7.81 Reject H0

1.4 γ=μ=η=0 3 25.77 10.50 7.04 Reject H0

1.5 μ=0 1 -1.92 6.63 3.84Do not

Reject H0

1.6 η=0 1 16.27 6.63 3.84 Reject H0

1.7 μ=η=0 2 25.77 9.21 5.99 Reject H0

It is observed that coefficient of ln(L)t is negative but insignificant. However, ln(K)t

and ln(E)t are negative and highly significant at 1% level. It suggests that technological

progress in Indian mining industry is energy and capital saving. Following Berndt (1990),

the energy and capital saving bias as obtained from this estimation can be defined as

17

proportional use of energy and capital, which is lesser than the average proportional use

of all inputs over the period. Therefore, this study reveals an energy and capital saving

technological progress in metallic mining industry of India. The possible factors behind

the energy saving technological changes may be promotion and adoption of green energy

techniques in the mineral extraction process. In other words, the metallic mining industry

seems to be following energy and capital conservation practices in their operation.

In this section, the appropriate model is selected for further estimation of productivity

growth in the metallic mining industry in India. Productivity estimation and

decomposition made in this paper are based on the time varying stochastic frontier model

with inefficiency effect, following a half normal distribution. In the following section,

TFP growth has been decomposed and explained in details.

5.1. Productivity growth decomposition for metallic mining

The novelty of this paper rests with the estimation of TFP growth in a decomposition

formulation for metallic mining industry of India. TFP growth for this analysis is

approach through adding the components such as technological progress, technical

efficiency change and scale components. As stated earlier, estimation of technological

progress is based on time trend. As a result, all the unseen factors not captured through

the functional relationship but varying over time is captured by technological progress.

Table 5: Annual Average TFP Growth Decomposition

Year TechnologicalProgress

Technical Efficiency Change

Scale Component

Total Factor Productivity Growth

1989 5.53 0.14 3.07 8.751991 5.00 0.21 0.54 5.761993 4.29 0.31 0.06 4.671995 4.13 0.46 0.23 4.841997 3.87 0.69 0.08 4.651999 3.90 1.02 0.02 4.952001 3.66 1.51 -0.38 4.792003 3.36 2.23 1.02 6.622005 2.95 3.30 0.11 6.37

18

2007 2.70 4.88 0.18 7.772009 2.33 7.21 -0.12 9.422011 2.04 10.66 0.31 13.022013 1.89 15.75 0.16 17.812014 1.80 19.15 0.36 21.32

Source: Author’s estimation Note: Value presented in the table are in percentage (%)

Technological progress is estimated to be 5.53% during 1989 but over time a

declining trend is observed for the Indian metallic mining industry. It declined to 3.90% in

1999 and further to 2.95 % in 2005. The recent figure of technological progress for 2014 is

estimated at 1.80 % level. However, a complete reverse picture is observed for technical

efficiency change. In 1989, the figure for TEC is estimated at 0.14 %, which continues at

less than 1% level until 1998. The reported figure for 2009 and 2014 reflects an upward

movement of TEC growth in later half of the study. Scale component reflects the change

in input use on output growth. In most of the years during the study period, estimated scale

component remains less than 1 %. Based on the addition of the components, the reported

figures for annual average of TFP growth in metallic mining is at 8.75 % in 1989.

However, a sharp decline in the technological progress has led to decline in annual

average TFP growth. But at the same time, improvement in the TEC after 2001 has

improved the TFP growth in Indian mining industry. As a result, the 4.95 % of TFP

growth in 1999 has improved to 9.42 % and 21.32 % in 2009 and 2014 respectively. The

share of each components over the year in TFP growth has been presented in Figure 3.

Figure 3: Composition of TFP Growth in Metallic Mining

19

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 20140%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SC

TEC

TP

The composition of TFP growth during the study period passes through an increasing

phase from 1989 to 1993, during which an increasing share of technological progress is

found in TFP growth. A continuous decline in the share of technological progress in TFP

growth is observed for the metallic mining from 1995. However, on the other hand, TEC

is moving upward from the early year of the present study. The largest determinant of

productivity growth has changed from TP in initial years to TEC in the recent period,

whereas scale component remaining at low level reflects that input changes have very

negligible impact on output growth. Further, the positive value of scale component, except

in the year 2001 and 2009, reveals the presence of increasing returns to scale in metallic

mining industry. The trend of TFP growth and its components are compared in Figure 4.

Figure 4: Trend of TP, TEC, Scale component and TFP growth

20

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2014

-5

0

5

10

15

20

25

Technological progress Technical efficiency changeScale Component TFP Growth

Val

ue in

Per

cent

age

(%)

Source: Author’s estimation

The TFP growth depicted in the above figure tells an interesting story on the

productivity growth path in metallic mining during the study period from 1989 to 2014. As

reflected from the graphical presentation, the whole period of analysis can be divided into

three sub-periods, based on movement of TFP growth. The first phase consists of

deceleration in TFP growth, which persists from 1989 to 1993. Secondly, a stable TFP

growth is seen during 1993 to 2001. Thirdly, an upward movement is experienced after the

year 2001. The decline in productivity growth in the first phase is largely due to the scale

component. But attainment of stability in TFP growth after 1993 is supposed to be

achieved through the adoption of national mineral policy which promotes exploration

activities in India.

Investment in skill enhancement programme for the existing human resource and

facilitation of specialised education facility for future human resource requirement in

mining are major focus areas of National Mineral Policy in 1993. The upward movement

in TEC after 1995 could be an indication of the impact of short-term skill enhancement

programmes, taken into account through the National Mineral Policy. However, achieving

21

future human resource requirement is a long-term goal, which requires time to reflect

through different indicators. Although TP is declining, the force of upward movement in

TEC outweigh the TP since 2001 resulting to upward movement in TFP growth for

metallic mining industry.

As stated above, TFP growth in metallic mining reflects a changing scenario of the

industry during the study period. Similarly, Indian economy formulated various policies

during the period to which the present study deals with for productivity analysis in

metallic mining industry. The major policy decisions change occurred during the period of

analysis are relating to the economic reforms initiated in 1991, adoption of National

Mineral Policy (NMP) in 1993 and complete allow of Foreign Direct Investment (FDI) in

mining sector from 2006. In order to capture the possible impact of policy change on TFP

growth, the whole period is segregated considering above implemented policies. The

details of average productivity growth in each of the sub-periods have been presented in

Table 6.

Table 6: Comparison of Average TFP Growth in Sub-periods

Division Period Classification Criteria Average TFP growth (%)

1 1989-1993 Pre-adoption of National Mineral Policy 6.081994-2014 Post-adoption of National Mineral Policy 8.20

2 1989-2000 First decade of economic reforms 5.302001-2014 After a decade of economic reforms 9.92

3 1989-2005 Prior to complete allow of FDI in mining 5.352006-2014 During complete allow of FDI in mining 11.70

4 1989-2014 Whole period of analysis 7.79Source: Author’s estimation

The estimation of average TFP growth in metallic mining by segregating the whole study

period into pre and post adoption of National Mineral Policy is an important aspect for

analysis. As reported in the above table, average TFP growth has increased from 6.08%

during 1989-93 to a higher level of 8.20% during 1994-2014. It could be the possible through

22

the impetuous focus of NMP on the research and development activity beside the mineral

extraction. Similarly, the possible impact of economic reforms on TFP growth in metallic

mining is observed through segregating period into first decade of economic reforms 1989-

2000 and after a decade of economic reforms 2001-2014. A comparative analysis of average

TFP growth between the periods reflects an improvement from 5.30% during 1989-2000 to

9.92% in the later period. It shows that productivity growth has largely improved after a

decade of economic reforms of Indian economy. Further, a comparative analysis of the

average TFP growth of metallic mining in the recent decade 2006-2014 with the preceding

period from 1989-2005 reveals an unprecedented TFP growth in recent decade. The TFP

growth for 2005-14 is 11.70%, which is 6.35 % higher than the figure of metallic mining for

the period 1989-2004. Metallic mining reflects a higher average TFP growth during the

second half of the study period. During 2005-14, average TFP growth accounted to be 3.91 %

higher than the TFP growth figure of whole study period, that is, 7.79%. It reflects the

possible impact of complete allow of FDI in mining sector in India.

In order to analyse the sources of TFP growth, decomposed components for each sub-periods

are presented in Table 7. It will not only reflect the stimulating source for TFP growth but

also useful for further policy-making.

Table 7: Decomposed Components of Average TFP Growth in Sub-periods

Division PeriodTechnologicalProgress (%)

Technical Efficiency Change (%)

Scale Component (%)

1 1989-1993 4.97 0.22 0.881994-2014 3.08 5.05 0.05

2 1989-2000 4.39 0.52 0.382001-2014 2.64 7.21 0.07

3 1989-2004 4.14 0.91 0.282005-2014 2.34 9.26 0.09

4 1989-2014 3.45 4.12 0.21Source: Author’s estimation

The results reveal that technical efficiency change, which played a nominal role prior to

the adoption of National Mineral Policy, has now become a major contributor in the TFP

23

growth with an average share of 5.05% during the period 1994-2014. This suggests that

the possible factor behind the increase in average productivity level is achieved by

enhancement in technical efficiency change through National Mineral Policy, 1993.

Further, decomposed components of average TFP growth for the two sub-periods

considering impact of economic reforms reveal a decline in average technological progress

from 4.39 % to 2.64 % and average scale components from 0.38 % to 0.07 % during the

two subsequent periods. Synthesis of the result reflects technical efficiency change as the

major factor behind steady improvement of productivity growth in metallic mining for the

last decade. However, the possibility of significant differences in input use, output, TFP

growth and its components after complete allow of FDI in mining sector has been verified

through using an independent dummy variable model. The details of the result is presented

in Table 8.

Table 8: Regression Result for Existance of Significant Differences in Two Time Period

IndependentVariable (Dummy)

Dependent VariablesOutput Labour Energy Capital TC TEC SC TFPG

β 6.86*** 5.39*** 5.14*** 6.96*** 2.34*** 9.26*** 0.09 11.70***

t-statistic 23.37 23.58 21.62 25.02 12.50 42.79 0.81 36.17p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.419 0.000F-stat 546.07

(0.000)556.07(0.000)

467.22(0.000)

625.91(0.000)

156.17(0.000)

1831.32(0.000)

0.65(0.419)

1308.2(0.000)

R2 0.48 0.48 0.44 0.51 0.22 0.76 0.07 0.70Source: Author’s estimationNote: *** indicate significant at 1% level

Perusal of the above table reveal that input, output, TFP growth and its components

during the period 2006-14 are significantly different from the period 1989-2005. In

metallic mining, the input use as well as the output has significantly changed in the recent

decade as compared to the previous period. Further, the TFP growth and its elements,

except scale component, are significant at 1% level, which reflects the changing scenario

24

of metallic mining industry in India in recent period. All the estimated models except SC

are found to be best fit, based on the F-statistic presented in the above table. However, the

low R2 value in many of the regression models are due to the presence of a single

explanatory variable.

6. Conclusions

In this study, using stochastic frontier decomposition technique, productivity growth

has been estimated for Indian metallic mining. Adoption of a decomposition technique for

the analysis of TFP growth has an advantage of providing information on the possible

sources of productivity growth. Empirical analysis of the TFP growth has been

undertaken, based on the parameter estimated through a translog production function for

metallic mining in India. Results of the study reveal that metallic mining has achieved

higher TFP growth in recent period. A comparison of average TFP growth in recent

decade has shown a substantial improvement in TFP growth of metallic mining as

compared to the decade after adoption of NMP. In recent period, the significant higher

growth in TFP is largely due to TEC.

Policy decision regarding productivity growth improvement, without prior idea on

drivers of productivity growth, may misdirect a policy maker. The TFP decomposition is

directed for suitable policy implication keeping in mind the drivers of productivity growth

in metallic mining. In this regard, the present study will be helpful for the policy makers to

develop effective policies regarding metallic mining sector in India. It could be suggested

that metallic mining industry in India is required to focus on investment in innovation and

upgradation of existing technology to enhance productivity growth further, which will be

helpful for not only the mining sector but also other sectors connected through forward

and backward linkages in the Indian economy.

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