119

CHAPTER-V

PRODUCTIVITY IN INDIAN MANUFACTURING SECTOR:

PRE- AND POST-REFORM COMPARISON

5.1 INTRODUCTION

In 1957, Robert Solow published a paper wherein he found empirically that

for the period 1909 to 1949, 87.5 percent of growth in the United State‟s (US) gross

output per man-hour was due to the „technical change‟ or productivity increase. He

explained this by distinguishing two main sources of output growth: (i) growth due to

the contribution of capital and labour inputs and (ii) due to the rate of productivity

growth. In which, the former does not lead to any change in the production function

while the latter lead to the shifting of the production function indicating technical

efficiency which could be due to change in technology, learning by doing, capacity

utilization, economies of scale etc (Ahluwalia, 1991).

The rate of technical change is, thus generally identified with the proportionate

amount of shift over time in the aggregate production function. This could be

measured empirically as a „residual‟ between the growth of output and the weighted

sum of inputs (Haltmaier, 1984).

This ice breaking revelation33

provided the theoretical foundation for almost

all the subsequent work on productivity measurement (Hall, 1989). But, there is a

great controversy regarding the methodology used for estimating the total factor

productivity (Hulten, 2000; Trivedi et al., 2000). Even, though, the rate of growth of

productivity in the industrial sector has been put forward as the key phenomenon in

determining the sectoral evolution (Pack, 1988). But to achieve the high productivity

growth, the „appropriate policy framework‟ is required, regarding which, no

consensus is found in the literature (ibid).

33 Solow was not the first to tie the aggregate production function to productivity. The link goes

back at least as far as Tinbergen in 1942 (Hulten, 2000). However, Solow‟s seminal

contribution lay in the simple, yet elegant, theoretical link that he developed between the

production function and the index number approach (ibid).

120

During the 1980s, many developing countries abandoned their inward-looking

development strategies for liberalization programmes. The supporters of these reforms

claimed that these moves would enhance productivity in domestic industries (Pavcnik,

2002) accompanied with the increase in technical efficiency in production. However,

it was found that much of the anticipated benefits have failed to materialize and in

some cases their realization has had a perverse effect (Pack, 1988).

To, reiterate, India too abandoned its „inward looking policy‟ in 1991 with the

adoption of the „structural adjustment policy‟ prescribed by „Bretton Woods twins‟,

with the implicit aim of enhancing the productivity and efficiency in the industrial

sector. A huge literature exists that studies the impact of reforms on the productivity

of Indian manufacturing sector. Some studies (Unel, 2003; Narayanan, 2004; Banga,

2004; Taneja et.al, 2007) have found that the total factor productivity has increased in

the post-reform period as compared to the pre-reform period while others (Trivedi et

al, 2000; Ahluwalia, 2006; Goldar, 2004; 2006; Kumari, 2006) have found the

opposite results. Thus, a clear consensus about the total factor productivity

performance in the post-reform as compared to the pre reform period does not exist.

There could be several reasons for these dichotomous results. The difference

in the methodology adopted by these studies could be one of the reasons. Even while

adopting a same method, the divergences were there regarding the construction of

variables. For example, in using the growth accounting method there is a divergent

views regarding the measurement of real value added (controversy between

Ahluwalia, 1991 and Balakrishan and Pushpangadan, 199434

). Secondly, the

controversy also looms on the choice of inputs (output or value added; net or gross35

;

34 A very comprehensive study on the subject while taking into account the 63 three-digit

industries is under-taken by Ahluwalia (1991). She found that the TFPG increases in the early

1980‟s due to the liberal moves of the government. But Balakrishan and Pushpangadan (1994)

refuted the claims of the „turnaround‟ by Ahluwalia (1991) in the productivity growth in the

Indian manufacturing sector as they found a serious lapse in the single deflation method used

in the latter‟s study and they introduced the double deflation method in which the nominal

output is deflated by output price index and the nominal material inputs by the input price

index. Following this, several subsequent studies used the double deflation method (Mitra,

1999; Trivedi et al., 2000; Goldar, 2006).

35 Regarding the controversy between the choice of gross and net values, Goldar (1986)

emphasised the former on the ground of unreliable „depreciation‟ figures reported in the

published data.

121

two inputs (Goldar, 1986) verses three inputs (Pradhan and Barik, 1998)). Further, the

measurement of capital stock is most controversial36

(Trivedi et al., 2000) and so does

the rate of its discarding37

. Thus, the literature shows that there is huge controversy

regarding the measurement of TFP and also of the impact of reforms on TFPG in the

manufacturing sector in India. So, the present chapter aims to scrutinize the optimistic

views of the proponents of reforms by estimating the TFPG in the pre- and the post-

reform period afresh.

Two methods are used for the analyzes: growth accounting method and the

frontier production approach. While all the above mentioned studies used the former

approach in which the technological progress is itself regarded as the measurement of

total factor productivity by assuming implicitly that the industries are producing at the

frontier (see, Nishimizu and Page, 1982; Kalirajan et al., 1996). Since industries do

not operate on their frontiers due to various non-price and organizational factors, but

somewhere below the frontiers, technical progress cannot be the only source of total

factor productivity growth (Kalirajan et al., 1996). Thus, the decomposition of total

factor productivity growth into technological progress and changes in technical

efficiency becomes substantial with a view of analyze whether technical efficiency

has improved after adopting the reforms. However, very few studies have taken this

aspect in to its preview. Important exceptions are the study by Mitra (1999) and

Goldar et al. (2004). While the study by Mitra (1999) estimated the technical

efficiency for all Indian industries from 1976 to 1993, the study by Goldar et al.

(2004) concentrated only on the engineering firms. Thus, a huge gap exists in the

literature regarding the measurement of TFPG by the stochastic frontier approach and

specifically on the impact of reforms on technical efficiency of the sector. An attempt

is made in this direction in the present chapter by measuring the technical efficiencies

of the various technological intensive sub-groups.

36 One reason could be the divergent benchmark capital stock and the other reason could be the

preparation of the capital stock series, in which the depreciation is added; whose published

data is found unreliable in case of Indian manufacturing sector (Goldar, 1986).

37 The rate of discarding varies massively in different studies. 2 in Goldar, 1986; 2.6 in Goldar,

2006; Kumari, 2006.

122

The structure of the chapter is as follows. Besides the present section, the next

section estimates the TFPG using the growth accounting method. Section 5.3

estimates technical efficiency using frontier production function approach. Section 5.4

finally concludes the chapter while presenting the main findings.

5.2 TFPG: GROWTH ACCOUNTING METHOD

Translog index method has been used for estimating the total factor

productivity growth (Chapter 3). The comparative analysis between pre- and post-

reform period is done using single kinked method. The results are presented in the

Table 5.1 followed by the Figures 5.1 and 5.2.

Table 5.1 shows that amongst the HT industrial sub-group all except two

three-digit industries namely electrical valves and tubes (321) and medical appliances

(331) witnessed a deceleration in the trend growth rate in the post-reform period as

compared to the pre-reform period. The greatest deceleration was seen in case of

pharmaceuticals (2423), office accounting and computer (300), watches and clocks

(333) and Aircrafts & spacecrafts (353) wherein their trend growth rate fell from 3.77

percent, 2.02 percent, 6.5 percent and 4.7 percent to 1.11 percent, 0.8 percent, 2.4

percent and 1.3 percent, respectively in the post-reform period.

The picture was similar in case of the MHT industrial sub-group, wherein

again the majority of the industries witnessed a deceleration in the trend growth rate

in the post-reform period as compared to the pre-reform period. However, the greatest

deceleration was witnessed in case of electricity distribution (312), insulated wires &

cables (313), motor vehicles (314) and also in case of the transport equipment (359).

In case of MLT industrial sub-group, all industries witnessed a deceleration in

the trend growth rate in the post-reform period as compared to the pre-reform period

except rubber products (251) which saw a very nominal increase in the trend growth

rate from -0.4 to 0.1 percent in the latter period as compared to the earlier one.

123

Table 5.1 Total Factor Productivity Growth (Pre and Post Reform Period

Comparison)

(Percentage)

NIC’04 Code Industry Pre-reform Post-reform

High Technology Industries

2423 Pharmaceutical 3.77 1.11

300 Office, Accounting & computer 2.02 0.8

321 Electrical valves & tubes -0.6 0.1

322 TV & Radio transmitters 1.41 0.3

323 TV & Radio receivers 0.6 0.3

331 Medical appliances -1.98 0.1

332 Optical instruments 0.9 0.9

333 Watches and clocks 6.5 2.43

353 Aircrafts and Spacecrafts 4.71 1.31

High Technology Industries 1.6 0.6

Medium- High Technology Industries

241 Basic chemicals 1.61 0.4

242*

Other chemical products 1.81 0.6

243 Manmade fibers - -0.39

291 General purpose machinery 1.11 0.7

292 Special purpose machinery 1.11 0.6

293 Domestic appliances 0.0 0.1

311 Electronic motors etc 1.31 1.01

312 Electricity distribution & control app. 2.22 -0.2

313 Insulated wires & cables 2.02 0.9

314 Accumulators, cells etc 0.1 0.1

315 Electronic lamps etc. - -

319 Other electrical equipment - -

341 Motor vehicles 2.12 1.31

342 Bodies for motor vehicles - 1.51

343 Parts for vehicles - -

352 Railways and tramways etc. 0.2 0.2

359 Transport equipment n.e.c 1.41 0.8

Medium- High Technology Industries 1.11 0.6

Medium- Low Technology Industries

231 Coke-oven products 5.76 2.33

232 Refined petroleum products 2.74 1.71

124

233 Process of nuclear fuel - -

251 Rubber products -0.4 0.1

252 Plastic products 2.22 1.01

261 Glass & glass products 2.74 1.21

269 Non-metallic minerals 2.84 1.21

271 Basic Iron ore & steel 0.9 0.3

272 Basic & non-ferrous metal 1.31 0.4

273 Casting of metals - -

281 Structural metal etc. 2.74 1.31

289 Fabricated metal etc. 0.9 0.5

351 Building & repair of ships 2.43 1.01

Medium- Low Technology Industries 0.7 0.8

Low Technology Industries

151 Production & process of meat -0.2 0.0

152 Dairy products -3.15 -1.3

153 Grain mill products 0.0 0.1

154 Other food products -2.9 -1.2

155 Beverages 0.1 -0.01

160 Tobacco products 0.9 0.4

171 Spin, weaving of textiles 0.6 0.3

172 Other textiles 0.8 0.6

173 Knitted & crochet fabrics 0.8 0.3

181 Wearing apparel, not fur 1.41 0.2

182 Dressing & dyeing of fur - -0.5

191 Leather 0.7 0.2

192 Footwear -1.9 -0.3

201 Saw milling of wood 1.11 0.3

202 Wood, corks & straw 1.51 1.01

210 Paper & paper products 2.63 1.11

221 Publishing 0.8 0.5

222 Printing 1.61 0.8

223 Reproduction of recorded media - -

361 Furnishing -2.66 -0.1

369 Manufacturing n.e.c. jewellery 1.1 0.5

Low Technology Industries 0.1 0.2

Total Organized Manufacturing Industries 0.5 0.4

Notes: Refer Appendix I for industry names.

Data source: EPWRF (2004), ASI (CSO) 2004; 2005.

125

Amongst the LT industries, the food products (151-160) showed throughout a

very low trend growth rate in both the sub-periods. Also, disappointing was the trend

growth rate of textile industries (171-181), which although had a very low growth rate

in the pre-reform period, decelerated further in the post-reform period. Similar were

the case of many industries. The overall picture of LT industries show that these were

having a very low TFPG in the pre-reform period (negative for 6 industries, 0<1 for

another 7; and 1<2 for another 5) which turned even grim in the post-reform period

(negative for 6, 0<1 for 12; and 1<2 for 2).

To have a comparative analysis of the TFP in the pre- and the post reform

period, the Figure 5.1 is presented. It shows that all the industries above the diagonal

have a higher TFPG in the post-reform period as compared to the pre-reform period.

Figure 5.1 Total Factor Productivity: Pre- and Post Reform Comparison

(Disaggregated Analyzes)

Notes: Figures are based on the estimates of TFPG presented in Table 5.1.

Data source: EPWRF (2004), ASI (CSO) 2004; 2005.

126

Thus, the overall picture (Figure 5.1) shows that only 19.6 per cent, that is 10

out of 51 industries38

accelerated in the post-reform period as compared to the pre-

reform period. Thus, a huge number of industries that is 41 out of 51 witnessed a

deceleration in the TFPG in the post-reform period.

Figure 5.2 Total Factor Productivity: Pre-and Post-Reform Period

Notes: Based on Table 5.1.

Data source: EPWRF (2004), ASI (CSO) 2004; 2005.

Further, the Table 5.1 and the corresponding Figure 5.2 shows that the rate of

TFPG showed a deceleration in the post-reform period as compared to the pre-reform

period. The TFPG of all the organized manufacturing industries was 0.5 per cent in

the earlier period which decelerated by one per cent in the latter period. Except for the

MLT and LT low technology industries; (both accelerated by 0.1 percent in the post-

reform period as compared to the pre-reform period) the other sub-groups witnessed a

deceleration in the TFPG in the post-reform period as compared to the pre-reform

period. The highest fall was although was witnessed in case of the high technology

industries from 1.6 percent in the pre-reform period to 0.6 percent in the post-reform

period.

38 Data for nine industries with NIC‟04 codes 182, 223, 233, 243, 273, 315, 319, 342, 343 is not

available.

127

Comparison of the results with similar studies

The following Table 5.2 shows the results of the similar studies that have used

the similar method for estimating the TFPG for the organized manufacturing sector.

Table 5.2 Comparison of the Results with Similar Studies (Growth

Accounting)

Author Sample Method Base Year Period Results

Unni et al.

(2001)

Organized

manufacturing

Industries

Double

deflation

1981-82 1978-85

1985-90

1990-95

-0.26

4.00

-1.28

TFPG

decelerated in

post-reforms

Das (2004) Organized

manufacturing

Industries

Gross Output

Function

1981-82 1980-91

1991-00

7.30

-0.8

TFPG

decelerated in

post-reforms

Ahluwalia

(2006)

Organized

manufacturing

Industries

Single

Deflation

1970-71 1980-91

1991-92

1992-98

3.8

-7.8

3.4

TFPG

decelerated in

post-reforms

Goldar

(2006)

Organized

manufacturing

Industries

Double

Deflation

Gross output

function

1981-82 1981-90

1991-98

1981-98

1981-90

1991-98

8.97

2.92

5.97

2.13

0.90

TFPG

decelerated in

post-reforms

Present

Study

Organized

manufacturing

Industries

Single

Deflation

1993-94 1980-91

1992-06

0.5

0.4

TFPG

decelerated in

post-reforms

The studies chosen in the Table 5.2 are those has used the organized

manufacturing industries as the unit of analysis and has used ASI (CSO) database. But

the results of all the studies varied a lot. The present study varied with the other

studies probable due to the following reasons. The results of Unni et al. (2001) and

Goldar (2006) were based on the single deflation method, are thus, different in the

methodology used by the other studies that used double deflation. Also, the period of

study for Unni et al. (2001) and Goldar (2006) is somewhat smaller than the present

study. The difference in estimating the capital stock also produces divergent results.

The use of different base years also produces different results (Goldar, 2006). But

despite the different empirical results, all the studies show that the TFPG of the

128

organized manufacturing industries decelerated in the post-reform period as compared

to the pre-reform period. However, the estimation of TFPG by Goldar (2006) using

gross output function and Unni et al (2001) also produces the rate of TFPG of less

than one for the post-reforms period which are somewhat similar to the present study.

5.3 TFPG: FRONTIER PRODUCTIONS FUNCTION APPROACH

Growth accounting provides a breakdown of observed economic growth into

components associated with changes in factor inputs and a residual that reflects

technological progress and other elements (Barro, 1999) like better utilization of

capacities, learning by doing, improved skills of labour etc. reflecting the „efficiency‟

with which the known technology is applied to production (Nishimizu and Page,

1982). Thus, in the broad sense, the concept of efficiency is used to characterize the

utilization of resources (Kumbhakar, 1989). Conventionally, the production function

postulates a well-defined relationship between a vector of maximum producible

outputs and a vector of factors of production. Comparatively, the „frontier‟ or the

„best practice‟ production function can be defined as the one that gives maximal

output, given the set of input quantities. It is the „technological progress‟ which shifts

the production functions. However, the distance from the frontier production function

of any observed production function defines „inefficiency‟.

To be more precise, by efficiency of a production unit, it means a difference

between observed and optimal values of its output and input (Lovell, 1993). The

comparison can take the form of the ratio of observed to maximum potential output

obtainable from the given input, or the ratio of minimum potential to observed input

required to produce the given output, or some combination of the two (ibid). The

former can be regarded as the output maximizing function while the latter as the input

minimizing functions. Thus, the „optimal‟ or „frontier production function‟ is a

regression that is fit with the recognition of the theoretical constraint that all

observations lie below it (Green, 1997). An efficiency measure emerges naturally

from the frontier production model as the distance between an „actual production

function‟ and the empirical estimate of the theoretical ideal, that is „frontier

production function (ibid).

129

Further, efficiency is of three types, „allocative efficiency‟ which studies the

process and policies that distribute resources among activities and sectors so they are

put to their best uses (Caves and Bailey, 1992). The second is „scale efficiency‟ which

encompass producing an output level by equating the production price with marginal

cost in the profit maximizing framework (De, 2004). The third is „technical

inefficiency‟ or „productive inefficiency‟ in which the analysis is based on measuring

the distance between the actual and the frontier production function (see Caves and

Bailey, 1992; Greene, 1997). Thus, by definition, technical inefficiency is the

discrepancy of the actual output level from the production frontier (Caves and Bailey,

1992). But under the free trade regime, technical inefficiency results when industries

that could compete with imports use more inputs per unit than is technically necessary

(Pack, 1988).

Theoretical Framework

The measurement of efficiency formally began with the pioneer work of

Farrell (1957), before which the average labour productivity, efficiency indices, cost

comparisons (Farrell, 1957) were popularly used for measuring efficiency. However,

Farrell‟s approach was based on „deterministic‟ frontiers which do not allow for

random shocks in the production process which are outside the control of the firm and

as such few extreme observations determine the frontier and exaggerate the maximum

possible output given inputs (Lee, 1983). However, Aigner et al (1977) and Meesun

and van den Broeck (1977) handled this problem with a more satisfactory conceptual

basis by explicitly including an error component which is stochastic, to capture the

inefficiency across the production unit (Lee, 1983).

Thus, there is a choice amongst the two basic methodologies for estimating

inefficiency, that is, the former „deterministic‟ or the latter „stochastic‟ frontier

approach. However, the latter seems more superior on the theoretical grounds due to

the inclusion of statistical noise resulting from events outside the firm‟s control such

as luck and weather (Bauer, 1990). But choosing the latter, pose another problem of

130

choosing the appropriate type of functional distribution amongst the four types of one

sided distributed error components viz. half-normal, exponential, truncated normal

distribution and gamma distribution; as the different specifications do give different

estimates (Lee, 1983). But, langrange multiplier tests were developed by Lee, 1983

and Schmidt and Lin, 1984 to make the appropriate choice about the one –sided

distributions.

However, with the development of techniques to use panel data to estimate

frontier functions following the study by Pitt and Lee (1981), it was found that the

specific distributional assumptions may be avoided, although then a model of time

varying efficiency must be imposed (Bauer, 1990).

The „time-varying inefficiency‟ models follow the untenable time-invariant

inefficiency models in which the inefficiency could be modeled as being statistically

independent over time. Cornwell, Schmidt and Sickles (1990) were first to develop an

approach in which the intercept as well as slope coefficients are allowed to vary over

time.

The next in line is to make a choice between the appropriate functional form,

that is the choice between the Cobb-Douglas and the Translog functional form.

However, Bauer (1990) has found that if one move very much beyond the former,

statistical efficiency is lost by estimating an overly flexible functional form.

Thus, it becomes unambiguous that measuring „efficiency‟ entails many

complexities, but the theoretical advancements entails new paradigms towards

ascertaining this.

Maximum-likelihood estimates for the parameters of the stochastic frontier

production function (Chapter 3) for the 5139

manufacturing industries for the period

1980-81 to 2005-05 as well as for the pre-reform period and the post-reform period

are presented in Table 5.3.

39 Industries with the NIC‟04 codes 182, 223, 233, 243, 273, 315, 319,342, 343 are dropped

from the analysis to make the dataset balanced.

131

Table 5.3. Stochastic Frontier Production Function Estimates

Dependent Variable: Net Value Added

Independent

Variables

Organized Industries High Technology Industries Medium-High Tech Medium-Low tech Low-Technology Industries

Pre-

reform

Post-

Reform

1980-06 Pre-

reform

Post-

Reform

1980-

06

Pre-

reform

Post-

Reform

1980-

06

Pre-

reform

Post-

Reform

1980-

06

Pre-

reform

Post-

Reform

1980-06

Production Function

Constant (β

0) 1.36

(1.36)

0.42

(0.43)

1.09***

(2.86)

1.32***

(5.39)

-0.54

(-1.1)

0.9***

(4.5)

1.52***

(5.3)

0.49**

(1.98)

0.97***

(4.6)

3.28***

(4.51)

3.12***

(6.15)

3.17***

(6.9)

1.41***

(10.2)

0.63**

(2.4)

1.21***

(8.66)

Log Labour

(β

1)

0.59

(0.83)

0.43

(0.59)

0.51***

(8.41)

0.89***

(10.5)

0.32***

(4.43)

0.66***

(14.2)

0.61***

(7.92)

0.52***

(11.7)

0.69***

(17.3)

0.29**

(2.13)

-0.35***

(-3.5)

-0.09

(-0.12)

0.75***

(21.3)

0.51***

(10.7)

0.61***

(17.5)

Log Capital

(β

2)

0.35

(0.5)

0.54

(0.92)

0.44***

(7.59)

0.02

(0.2)

0.71***

(9.03)

0.3***

(6.63)

0.32***

(4.6)

0.45***

(11.9)

0.28***

(8.4)

0.48***

(4.8)

0.97***

(14.6)

0.73***

(23.8)

0.19***

(6.45)

0.45***

(11.2)

0.33***

(11.8)

Year (β

3) -0.02

(-0.15)

-0.02

(-0.1)

-0.1

(-1.31)

0.07***

(2.98)

0.01

(0.87)

0.02***

(3.2)

-0.02

(-1.04)

0.04***

(4.4)

0.01***

(2.8)

-0.05

(-1.5)

-0.02

(-0.3)

-

0.03***

(-8.2)

0.06

(0.95)

0.007

(0.09)

-0.08**

(-2.3)

Inefficiency Model

Constant

(δ0) 0.02

(0.03)

0.002

(0.002)

-0.5***

(-4.07)

-5.34

(-1.4)

-0.02

(-0.2)

-7.9

(-1.5)

-2.84

(-0.66)

-4.8*

(-1.8)

-6.54

(-0.30)

-0.72

(-1.31)

-0.73***

(-5.07)

-

0.25***

(-4.4)

-2.19

(-0.66)

-0.3*

(-1.76)

-0.02

(-0.2)

t (δ1) 0.04

(0.37)

0.06

(0.14)

0.04***

(2.65)

0.9

(1.30)

1.22

(0.18)

0.77*

(1.8)

0.74

(0.92)

0.96**

(2.06)

0.26

(0.34)

0.43**

(2.17)

0.23***

(4.6)

0.07***

(4.8)

-1.1

(-0.84)

0.06***

(2.71)

0.02**

(1.96)

t2 (δ2) -0.01

(-1.07)

-0.05

(-0.43)

-0.01**

(-2.4)

-0.04

(-1.1)

-0.07

(-0.18)

-0.02*

(-1.9)

-0.06

(-0.91)

-0.04**

(-1.99)

-0.01

(-0.35)

-0.04**

(-2.23)

-0.01***

(-4.64)

-

0.03***

(-4.1)

0.08

(0.85)

-0.03***

(-3.7)

-0.01***

(-3.24)

σ2 0.23

***

(9.47)

0.19***

(7.4)

0.22***

(13.3)

0.37***

(3.36)

2.23

(0.19)

0.85*

(1.83)

0.73

(0.85)

0.27**

(2.5)

2.00

(0.35)

0.5***

(9.5)

0.25***

(7.8)

0.38***

(11.9)

0.53

(0.94)

0.14***

(11.6)

0.11***

(14.1)

γ 0.07***

(5.8)

0.05***

(2.7)

0.0001

(1.04)

0.79***

(8.2)

0.96***

(6.42)

0.89***

(13.0)

0.96***

(19.1)

0.84***

(9.68)

0.97***

(14.0)

0.07

(0.91)

0.02***

(7.8)

0.00

(0.5)

0.89***

(7.5)

0.06

(0.88)

0.02***

(8.14)

Log-

likelihood -388.7

-

404.13

-

830.37 -36.5 -55.0

-

110.96 -46.9 -39.8 -105.8 -135.7 -107.7 -270.2 -14.5 -107.4 -149.11

No. of.

observations 612 714 1326 108 126 234 144 168 312 132 154 286 228 266 494

Notes: * , ** and *** indicates significant at 10percent, 5percent and 1percent level, respectively.

Figures in the bracket are the t values.

Data Source: EPWRF vol II and ASI(CSO), 2004-05 and 2005-06.

132

The result of the panel „time-varying inefficiency‟ model (Table 5.3) shows

that most of the coefficients of the model are significant. The analysis shows that the

coefficient of labour is positive and statistically significant for the organized

manufacturing industries for the whole period under study (1980-81 to 2005-06).

Quiet contrary with the pre-reform period, the post-reform period witnessed capital to

have a greater impact in determining „value addition‟ in the manufacturing industries.

The similar pattern was seen in case for the high technology (HT) industrial sub-

group wherein 0.89 percent increase in value-added was due to 1 percent increase in

labour employed in the pre-reform period which was reduced to 0.32 percent increase

in value-added due to the similar increase in labour employed in the post-reform

period; which is apparent due to the high capital demanding nature of this industrial

sub-group and the infusion of liberal policies for capital investment. However, in case

of MHT and LT industrial sub-groups, the coefficient of labour is positive and

statistically significant for the pre- and post-reform period. Although, these results are

in conjunction with the nature of the latter sub-group; but the dominance of labour in

case of the former sub-group show the low capital-labour ratio in the industrial sub-

group. In case of the MLT industries, the „heavy-industries bias‟ policies of the

Government for the pre-reform period and the liberalization policies regarding the

capital investment in the post-reform period lead capital to be a significant „value-

addition‟ factor for the period under study.

The time variable coefficient (β3) in the „production function‟ accounts for Hicks

neutral „technological change‟ signifying a small rightward shift in the „frontier

production function‟. Amongst the four technology-intensive sub-groups, the greatest

shift was witnessed in case of MHT industrial sub-group followed by MLT industrial

sub-group.

However, the coefficients of the „technical Inefficiency model‟ (Table 5.3) is

of much interest. The positive coefficient of „t‟ (δ1) suggest that „technical

inefficiency‟ remained in the organized manufacturing industries throughout the

period, although out of the four technology-intensive sub-groups, HT industries have

witnessed an increase in „technical inefficiency‟ in the post-reform period.

133

The remaining two parameters ζ2

and γ = ζ2u/(ζ

2u + ζ

2v) are associated with

the variance of the random variables vit and uit; of which a higher value of γ, close to

one indicates that the inefficiency effects are likely to be highly significant in the

analysis (Battese and Coelli, 1993). The value of γ is quiet high in case of the HT and

MHT industries which are significant at one percent level of significance signifying

that „inefficiency‟ exists in these industries to a large extent.

Further, two hypothesis tests were done (Table 5.4) using the generalized

likelihood ratio statistics which is defined as

ε = -2 log [L(H0)/L(H1)] ...............(1)

where L(H0) and L(H1) are the value of the likelihood function for the frontiers

models under the null and alternative hypothesis, H0 and H1, respectively. In large

samples this statistic has a chi-square (or a mixture of chi-square distribution) with the

degree of freedom equals to the difference between the parameters in the null and

alternative hypothesis.

Table 5.4 Tests for hypotheses for parameters of the Stochastic Frontier Models

LR chi2 Decision

H0: γ = 0

Organized Manufacturing

(1980-06)

4.39**

Reject H0

H0:γ=δ0 = δ1 =0

HT

Pre-Reform 11.09**

Reject H0

Post-Reform 10.32**

Reject H0

1980-06 19.46***

Reject H0

MHT

Pre-Reform 28.91***

Reject H0

Post-Reform 22.67***

Reject H0

1980-06 29.33***

Reject H0

MLT

Pre-Reform 10.99**

Reject H0

Post-Reform 11.07**

Reject H0

1980-06 13.24***

Reject H0

LT

Pre-Reform 2.23

Post-Reform 5.31

1980-06 14.70***

Reject H0

Organized

Manufacturing

Pre-Reform 6.67

Post-Reform 34.22***

Reject H0

1980-06 27.78***

Reject H0

Notes:

1. *** means significant at 1percent and ** means significant at 5 percent.

2. The critical values are taken from Kodde and Palm (1986) as the test statistics

follows a mixed Chi-square distribution.

3. The critical values are 8.761 and 12.483 for the 5 percent and 1 percent level of

significance.

134

The two hypotheses are tested, the first null hypothesis specifies that the

inefficiency effects are not stochastic, is rejected at 5 percent level of significance.

This is tested for the whole manufacturing industries for the period 1980-81 to 2005-

06 to ascertain whether stochastic model fits the data. Second null hypothesis (Table

5.4) specifies that the inefficiency effects are absent from the model, is strongly

rejected for most of the cases. Thus, the results show that the stochastic frontier model

with inefficiency effects is appropriately shows the inefficiency prevalent in the

organized manufacturing industries in India.

Further, to scrutinize the extent of Technological Efficiency (TE) in the Indian

organized manufacturing industries and its four technology-intensive sub-groups, the

mean technical efficiency is estimated using the maximum likelihood method for each

of the four subgroups separately. Since the estimates of inefficiency are conditioned

on the given technology (production frontier), it is imperative to estimate different

production frontiers for different technologies rather than pooling the data together

and estimate a single production function from which the technological efficiency is

estimated which would not represent either technology and any statement regarding

efficiency is likely to be wrong (Kumbhakar and Wang, 2010). Thus, Table 5.5 shows

the results estimated wherein the average TE should lie between 0 and 1. „One‟

signifies the technological efficiency while „zero‟ being the technological inefficiency

indicator.

Table 5.5 Average Technical Efficiency Change for Pre- and Post-Reform

Periods

1980-06 Pre-Reform Post-Reform Change in TE#

HT 0.79 0.84 0.75 -0.09***

MHT 0.77 0.75 0.79 0.04**

MLT 0.62 0.64 0.61 -0.03

LT 0.87 0.94 0.81 -0.13

Organized

Manufacturing 0.82 0.82 0.82 0.00

Notes: # means the difference between the pre- and post-reform period.

*** and ** means significant at 1 percent and 5 percent level of significance

calculated using the t-test.

Source: Calculated.

135

It is evident from the Table 5.5 that the average technical efficiency fell in all

technology-intensive sub-groups except MHT industries where it increases by 0.04

percent. The greatest fall was witnessed in case of LT industries followed by a highly

significant fall of 0.09 percent in case of the HT industries. But the overall picture

showed that despite the fall in TE in the post reform period as compared to the pre

reform period, LT industries had the highest TE as compared to other sub-groups.

This shows that the Indian organized manufacturing industries have the technical

efficiency in the LT industries. But to understand more about the technical efficiency

for each of the four subgroups, the following Table 5.6 is presented which provides

the average technical efficiency for the 3-digit disaggregated level industries.

Table 5.6 Annual Average Technical Efficiency for Indian Manufacturing Sector

NIC’04

Code

Industry 1980-81

to 2005-

06

Pre-Reform

(1980-81 to

1991-92)

Post-Reform

(1992-93 to

2005-06)

Change#

High Technology Industries

2423 Pharmaceutical 0.786 0.810 0.765 -0.04

300 Office, Accounting &

computer 0.871 0.882 0.861 -0.02

321 Electrical valves & tubes 0.800 0.825 0.778 -0.05

322 TV & Radio transmitters 0.809 0.869 0.758 -0.11***

323 TV & Radio receivers 0.864 0.868 0.861 -0.01

331 Medical appliances 0.840 0.895 0.793 -0.10***

332 Optical instruments 0.808 0.840 0.781 -0.06

333 Watches and clocks 0.725 0.832 0.634 -0.20***

353 Aircrafts and Spacecrafts 0.648 0.728 0.580 -0.15***

Medium- High Technology Industries

241 Basic chemicals 0.796 0.769 0.818 0.05

242 Other Chemical products 0.633 0.682 0.591 -0.09

291 General purpose machinery 0.835 0.831 0.838 0.01

292 Special purpose machinery 0.811 0.801 0.820 0.02

293 Domestic appliances 0.769 0.761 0.776 0.01

311 Electronic motors etc 0.880 0.904 0.860 -0.04**

312 Electricity distribution &

control app. 0.626 0.410 0.812 0.40***

313 Insulated wires & cables 0.849 0.847 0.851 0.00

314 Accumulators, cells etc 0.888 0.876 0.899 0.02

136

341 Motor vehicles 0.824 0.824 0.824 0.00

352 Railways and tramways etc. 0.581 0.571 0.589 0.02

359 Transport equipment n.e.c 0.816 0.768 0.856 0.09***

Medium- Low Technology Industries

231 Coke-oven products 0.645 0.691 0.606 -0.08

232 Refined petroleum products 0.645 0.691 0.604 -0.08

251 Rubber products 0.610 0.616 0.606 -0.01

252 Plastic products 0.645 0.691 0.606 -0.08

261 Glass & glass products 0.610 0.616 0.605 -0.01

269 Non-metallic minerals 0.610 0.616 0.605 -0.01

271 Basic Iron ore & steel 0.610 0.616 0.606 -0.01

272 Basic & non-ferrous metal 0.645 0.691 0.604 -0.08

281 Structural metal etc. 0.610 0.616 0.606 -0.01

289 Fabricated metal etc. 0.610 0.616 0.606 -0.01

351 Building & repair of ships 0.610 0.616 0.606 -0.01

Low Technology Industries

151 Production & process of

meat 0.856 0.938 0.785 -0.15

152 Dairy products 0.855 0.937 0.784 -0.15

153 Grain mill products 0.855 0.938 0.784 -0.15

154 Other food products 0.890 0.938 0.848 -0.09

155 Beverages 0.855 0.938 0.784 -0.15

160 Tobacco products 0.856 0.939 0.785 -0.15

171 Spin, weaving of textiles 0.889 0.937 0.848 -0.09

172 Other textiles 0.855 0.938 0.784 -0.15

173 Knitted & crochet fabrics 0.855 0.938 0.785 -0.15

181 Wearing apparel, not fur 0.856 0.938 0.785 -0.15

191 Leather 0.890 0.938 0.848 -0.09

192 Footwear 0.890 0.937 0.848 -0.09

201 Saw milling of wood 0.889 0.938 0.848 -0.09

202 Wood, corks & straw 0.890 0.938 0.848 -0.09

210 Paper & paper products 0.889 0.938 0.848 -0.09

221 Publishing 0.855 0.938 0.784 -0.15

222 Printing 0.889 0.938 0.848 -0.09

361 Furnishing 0.890 0.938 0.848 -0.09

369 Manufacturing n.e.c.

jewellery 0.856 0.939 0.785 -0.15

Notes: # means the difference between the pre- and post-reform period.

*** and ** means significant at 1 percent and 5 percent level of significance

calculated using the t-test.

Refer Appendix I for industry names.

137

The Table 5.6 depicts the average technical efficiency for the 51 industries

both for the pre and post reform period and the change therein. It was expected that

the reforms to have a positive effect in enhancing the technical efficiency of the

industries which were close to the frontier as these industries will put on effort to

remain competitive and efficient. Whereas the inefficient industries would be wiped

out and in the subsequent period and only efficient industries will remain. But the

results at the disaggregate level shows that the industries which were near the frontier

in the pre-reform period, that is, LT industries has seen a fall in their efficiency level

in the range of 0.9 to 0.15 percent. This shows that the earlier methods of production

became redundant and the industry remained reluctant in adopting and mastering the

new techniques. However, the industries that were having a low technical efficiency

in the pre reform period, that is, MLT industries became worse off than before in the

post-reform period. But certain industries from the MHT industrial subgroup have

seen a rise in their efficiency level in the post-reform period as compared to the pre-

reform period. These industries are chemical products (242), machinery (291, 292 and

293), electricity distribution (312), and transport (341, 352 and 359). On the other

hand, the average technical efficiency of all the nine HT industries fell in the post-

reform period as compared to the pre-reform period.

Further, the standard deviation was estimated (Appendix V.I) to know the

variability in the technical efficiencies amongst the various industries. From the

analysis, it was found that the variability is low amongst the HT industries which

indicate that when the average technical efficiency fell in the post-reform period as

compared to the pre-reform period, almost all industries have witnessed a fall in their

efficiency level indicating that HT industries failed to master the complex

technologies that keep on developing. However, in case of MLT and LT industries,

particularly in the post-reform period considerable variability was found.

To sum up, the overall picture remained gloomy as the average annual

technical efficiency for the whole of manufacturing industries for the pre and the post

reform period remained intact at 0.82.

138

5.4 CONCLUSION

The aim of the present chapter is to estimate and compare the technological

factor productivity growth (TFPG) in the pre-reform period and the post-reform

period with the motive to scrutinize the basic implicit view of expected increase in

TFPG after the adoption of the economic reforms of 1991. Two methods were used

for the analyzes. The first being the Growth accounting approach, which is used

extensively in the literature. But this method engulfs various methodological

controversies and thus, produces varied results as being estimated by various scholars.

Secondly, stochastic frontier production function approach is used to estimate the

extent of technical efficiency (TE) in the manufacturing industries.

The results, using the growth accounting approach show that the TFPG

decelerated in the post-reform period as compared to the pre-reform period for all the

industrial sub-groups except the LT industries wherein its rate remained intact,

although very low.

The results using panel dataset shows that there exists a inefficiency in most of

the industries, which rejects the hypothesis that this sector have become efficient.

Further the results also show that there is relatively high efficiency in the production

of relatively low-technology (LT) industries which put in jeopardy the question of

sustainability of the industrial sector since these industries have a lower income

elasticity of demand (Lall, 2001). Thus, for sustaining industrialization there is a need

to increase efficiency in the manufacturing industries, more so in the relatively high

technology industries that could lead to more production, sustainability and

employment generation. But for enhancing efficiency in the manufacturing sector,

„evolutionary technology policies‟ could be appropriate which regards learning as an

incremental and path-dependent process (Lall, 2001). These policies could be

appropriate for the industries that operate with imperfect knowledge of technology as

these policies emphasized on the need to put on effort and time to learn and become

subsequently efficient (ibid). Thus, to regard neoclassical paradigm of „outward-

oriented policies‟ as the only means of enhancing efficiency would not be an

139

appropriate policy initiative as has been evident in the present exercise. Thus, a

consistent mechanism of enhancing efficiency should be adopted. The policies should

encompass the use of new and complex technologies, new skills, education, training

and technology support system which can be developed by the „targeted policies‟ of

the Government (ibid). Thus, to conclude the chapter confirms the views of Stiglitz,

2006 “Without appropriate government regulation and intervention, markets do not

lead to economic efficiency”.