Measurement of Technological Change in India’s Textile Machinery Industry Using the Malmquist Productivity Index

Abstract

This article measures technological change in India’s textile machinery industry, and examines how user–producer interaction affects this. Employing the non-parametric Malmquist productivity index, we find there has been little technological change in the textile machinery industry from 1998–99 through 2007–08. It is proposed that poor and unsustainable demand and the shrinking share of domestic demand for textile machinery owing to the technological upgradation fund scheme—meant for providing interest reimbursements or capital subsidies to textile manufacturers—may have weakened the user–producer interactions, thereby bringing down innovative activities and innovations in the textile machinery industry in India.

JEL Classification: O14, O33, C14

Keywords

Textile Machinery Industry Technological Change Malmquist Productivity Index Data Envelopment Analysis

1. INTRODUCTION

The capital goods sector is at the heart of the generation and diffusion of technologies in an economy (Fransman, 1986; Rosenberg, 1963a, 1963b). Any change in process and product technologies invariably requires improved machinery and equipment (Fransman, 1986). Second, improved capital goods are essential for the successful diffusion of inventions. The history of invention indicates that many inventions were kept idle for a long time after their initial conceptualisation due to the lack of mechanical skills, facilities, design and engineering capacity which are necessary to materialise them into a working reality (Rosenberg, 1972). Promoting innovation in the capital goods sector is, therefore, an imperative to bring about innovation and diffusion in other sectors of the economy (Chudnovsky et al., 1983; Lee, 2000). In light of this, this article intends to examine technological change in India’s textile machinery industry—which is an important segment of the country’s capital goods sector.

Textile machinery manufacturing has an important bearing upon innovation and development in textile manufacturing, which has an overwhelming presence in the life of the country.¹ There are interactive and communicative processes between users (textile manufacturers) and producers (textile machinery manufacturers) of the textile machinery industry.² These symbiotic processes are very important as they bring improvements in processes or product innovation for the industry.³ Hence, the article measures technological change in textile machinery industry, and also examines how the user–producer interaction influences innovative activities in the textile machinery industry.

The article is organised in the following manner. The next section reviews the literature related to India’s textile machinery industry. The Indian textile machinery industry is profiled in Section 3. Section 4 discusses the methodology (i.e., the non-parametric Malmquist productivity index) for the empirical analysis. The data and construction of variables are elaborated in Section 5. Section 6 estimates the technological changes in the textile machinery industry, and analyses how the user–producer relationship is important for innovations in the textile machinery industry. The last section concludes and offers some policy suggestions.

2. RELATED LITERATURE

There is very little systematic academic literature on India’s textile machinery industry apart from a few newspaper and journalistic writings. The few studies on this industry are by Lall (1987) and Roy (2010), among others. Lall (1987) had taken two textile machinery manufacturers, namely, Lakshmi Machine Works (Coimbatore) and Star Textile Engineering Works (Mumbai), as cases to study textile machinery industry. His analysis reveals that technological developments in domestic textile machinery were dependent upon imitation (or reverse engineering) and licensing of foreign technologies, and that textile machinery makers were able to produce good quality machinery and equipment, but of older vintages.

In a study on textile manufacturing, Roy (2010) discusses the textile machinery industry during the pre- and post-economic reforms period, stressing the fact that economic liberalisation resulted in the importation of second-hand textile machinery as well as technological change in the domestic textile machinery industry in India.

It is evident there is an acute shortage of academic or scholarly research on India’s textile machinery industry to give policy makers a clear picture of textile machinery manufacturing. This article presents a scholarly analysis of textile machinery manufacturing to guide the government on new policies for the industry.

3. THE TEXTILE MACHINERY INDUSTRY IN INDIA

The textile machinery industry, according to the usage of different value chains of textile manufacturing, is classified into different segments, such as, spinning machinery, weaving machinery, processing machinery, finishing machinery, testing equipment and components and accessories.

3.1 Size and Distribution of the Industry⁴

There are around 1,446 textile machinery manufacturing units in India, 584 of which produce the complete machinery along with parts and accessories for textile machinery; the rest only manufacture parts and accessories. These textile machinery producing units are mostly clustered in Gujarat and Tamil Nadu—45 per cent in Gujarat and 31 per cent in Tamil Nadu.⁵

These units are distributed across different activities of the textile machinery industry; 51 per cent are engaged in the production of spinning machinery and weaving machinery (25.66 per cent in spinning machinery and 25.24 per cent in weaving machinery);⁶ processing machinery occupies 11.69 per cent of the units, followed by synthetic filament yarn machinery with 8.23 per cent, and ginning and pressing machinery with 3.15 per cent; 17.29 per cent of the textile machinery units are engaged in miscellaneous parts and accessories.

As mentioned, most of the production comes from Tamil Nadu and Gujarat, with them collectively contributing around 84 per cent of the production worth ₹ 4,854.74 crore in 2006–07. Tamil Nadu being the largest producers of the yarn in the country specialises in the production of spinning and allied machinery followed by testing equipment. Gujarat, on the other hand, specialises in the production of synthetic filament yarn machinery followed by weaving machinery, spinning machinery and processing machinery.

The distribution of production between small-scale industry (SSI) and non-SSI⁷ shows that SSIs, which comprise 59.61 per cent of all the textile machinery units, contribute only 32.12 per cent of total production, and the majority of the production comes from the non-SSIs which have 40.39 per cent of the total textile machinery manufacturing units. Most of the output (91 per cent) is consumed within the country and rest is exported.

4. METHODOLOGY

There are basically three approaches to measuring technological change in a particular sector or industry—the input approach (research and development expenditures), output approach (patents) and total factor productivity (TFP) change. We cannot apply either of the first two approaches because of the data limitation in India’s textile machinery industry, so we use the third approach, that is, TFP change. In this approach, technological change is estimated by decomposing TFP change into technological change and efficiency change (technical and scale efficiency change).⁸

There are a variety of approaches, parametric and non-parametric, which have been developed to estimate TFP change. Parametric models specify an explicit functional form for the frontier and econometrically estimate the parameters using sample data for inputs and output; hence, the accuracy of the estimates (efficiency, technological and productivity change) is sensitive to the nature of the functional form specified. Unlike parametric models, Data Envelopment Analysis (DEA) introduced by Charnes et al. (1978) and generalised by Banker et al. (1984) offers a non-parametric alternative to parametric frontier production analysis.⁹ DEA estimates the Malmquist productivity index and decomposes the Malmquist productivity change index into multiplicative factors such as efficiency change and technological change.

4.1 Malmquist Productivity Index

Caves et al. (1982) introduced the Malmquist productivity index.¹⁰ Distance function forms an inevitable component of Malmquist productivity index. It is discussed in detail later.

Assume that for each time period $t 1,, T$ , the production technology $S^{t}$ models the transformation of inputs $x^{t} (x_{1}, x_{2},, x_{N})_{}^{N}$ into output $y^{t} (y_{1}, y_{2},, y_{M})_{}^{M}$ .

S^{t} \{(x^{t}, y^{t}) : x^{t} can produce y^{t}\}

(1)

The production technology outlined above consists of the set of all feasible input/output vectors and also meets key axioms such as free disposability of input and output, convexity and constant returns to scale.¹¹ The distance function can be input-oriented (input-conserving) and output-oriented (output-augmenting). We focus on output-oriented distance function which considers the maximum proportional expansion of the output vector corresponding to a given input vector. It measures the distance of a firm from its production frontier—how close a particular level of output is to the maximum attainable level of output that could be obtained from the same level of inputs if production is technically efficient. Following Fare (1988) output distance function at t can be defined as

\begin{matrix} D_{o}^{t} (x^{t}, y^{t}) \inf \{: (x^{t}, y^{t} /) S^{t}\} \\ {(\sup \{: (x^{t}, y^{t}) S^{t}\})}^{1} \end{matrix}

(2)

and similarly for $D_{o}^{t 1} (x^{t 1}, y^{t 1})$ .¹², ¹³ The distance function completely describes the production technology and is equal to the reciprocal of Farrell (1957) output-based measure of technical efficiency.¹⁴ The superscript t associated with D refers to which period’s production frontier is used as reference technology, and the subscript associated with D indicates the output-oriented distance function. It should be noted that $D_{o}^{t} (x^{t}, y^{t}) 1$ if and only if $(x^{t}, y^{t}) S^{t}$ and $D_{o}^{t} (x^{t}, y^{t}) 1$ if and only if $(x^{t}, y^{t})$ is on the boundary or frontier of technology. This situation occurs when the production is technically efficient (Farrell, 1957).

To construct the Malmquist productivity index we need to define the output distance functions with respect to two different time periods, such as,

D_{o}^{t} (x^{t 1}, y^{t 1}) \inf \{: (x^{t 1}, y^{t 1} /) S^{t}\}

(3)

and

D_{o}^{t 1} (x^{t}, y^{t}) \inf \{: (x^{t}, y^{t} /) S^{t 1}\}

(4)

The first distance function, Equation (3), measures the maximum proportional change in output required to make $(x^{t 1}, y^{t 1})$ feasible in relation to technology in the previous period t. Similarly, the second mixed distance function, Equation (4), measures the maximum proportional change in output required to make $(x^{t}, y^{t})$ feasible in relation to technology t + 1. In both these mixed period cases, the value of the distance function may exceed unity. This can occur if the observation being evaluated is not feasible in the other period. The calculation of the output distance functions and how they can be used to give insights about efficiency change and technological change is illustrated in Figure 1.

Figure 1

Source: Färe et al. (1994: 70).

The Figure 1 depicts two production frontiers $S^{t}$ and $S^{t 1}$ at period t and period t + 1, respectively.¹⁵ Here, the observed production point $(x^{t}, y^{t})$ at t is interior to the boundary of technology at t, that is, it is not technically efficient. The value of the distance function for the observation $(x^{t}, y^{t})$ in terms of distances on the y-axis is $oa / ob$ , which is less than one. Similarly, the distance function for the observation $(x^{t 1}, y^{t 1})$ at t + 1, that is, $D_{0}^{t 1} (x^{t 1}, y^{t 1})$ , is $od / of$ and this is also less than one. If we compare these two distance functions we can ascertain the change in efficiency between time periods t and t + 1. The mixed period distance functions $D_{o}^{t 1} (x^{t}, y^{t})$ and $D_{0}^{t} (x^{t 1}, y^{t 1})$ can also be determined as $oa / oc$ and $od / oe$ , respectively (here, oc and oe are same). These mixed period distance functions are needed to measure technological change over time. Having discussed the distance function graphically, let us define the Malmquist productivity index.

Using period t benchmark technology, Caves et al. (1982) define the period t output-oriented Malmquist productivity index as¹⁶

M^{t} \frac{D_{o}^{t} (x^{t 1}, y^{t 1})}{D_{o}^{t} (x^{t}, y^{t})}

(5)

$M^{t}$ compares $(x^{t 1}, y^{t 1})$ to $(x^{t}, y^{t})$ by comparing their distances to the benchmark technology $S^{t} .$ Although $D_{o}^{t} (x^{t}, y^{t}) 1$ because $(x^{t}, y^{t})$ must be feasible for $S^{t},$ $D_{o}^{t} (x^{t 1}, y^{t 1}) 1$ because $(x^{t 1}, y^{t 1})$ may or may not be feasible for $S^{t}$ . Hence $M^{t} 1$ depending on whether productivity growth, stagnation or decline has occurred between periods t and t + 1, from the forward-looking perspective of period t benchmark technology.

Using period t + 1 benchmark technology, the period t + 1 output-oriented Malmquist productivity index can be written as

M^{t 1} \frac{D_{o}^{t 1} (x^{t 1}, y^{t 1})}{D_{o}^{t 1} (x^{t}, y^{t})}

(6)

$M^{t 1}$ compares $(x^{t 1}, y^{t 1})$ to $(x^{t}, y^{t})$ by comparing their distances to the benchmark technology $S^{t 1} .$ Although $D_{o}^{t 1} (x^{t 1}, y^{t 1}) 1$ because $(x^{t 1}, y^{t 1})$ must be feasible for $S^{t 1},$ $D_{o}^{t 1} (x^{t}, y^{t}) 1$ because $(x^{t}, y^{t})$ may or may not be feasible for $S^{t 1}$ . Hence, $M^{t 1} 1$ depending on whether productivity growth, stagnation or decline has occurred between periods t and t + 1, from the backward-looking perspective of period t + 1 benchmark technology.

Both indexes compare $(x^{t 1}, y^{t 1})$ to $(x^{t}, y^{t})$ , but use benchmark technology from different periods. The choice of benchmark technology is arbitrary, and the two indexes are not necessarily equal except under restrictive neutrality conditions on technical change. Indeed, one index may shows productivity growth and the other productivity decline. Fare et al. (1994), therefore, define the Malmquist productivity index as the geometric mean of the two, and write it as,

M_{o} (x^{t 1}, y^{t 1}, x^{t}, y^{t}) {[(\frac{D_{o}^{t} (x^{t 1}, y^{t 1})}{D_{o}^{t} (x^{t}, y^{t})}) (\frac{D_{o}^{t 1} (x^{t 1}, y^{t 1})}{D_{o}^{t 1} (x^{t}, y^{t})})]}^{0.5}

(7)

$M_{o} (x^{t 1}, y^{t 1}, x^{t}, y^{t}) 1$ according to whether productivity growth, stagnation or decline has occurred between periods t and t + 1. Now, using simple arithmetic manipulation, Equation (7) can be written as the product of two distinct components—technological change and efficiency change.

\begin{matrix} M_{o} (x^{t 1}, y^{t 1}, x^{t}, y^{t}) \frac{D_{o}^{t 1} (x^{t 1}, y^{t 1})}{D_{o}^{t} (x^{t}, y^{t})} \\ {[(\frac{D_{o}^{t} (x^{t 1}, y^{t 1})}{D_{o}^{t 1} (x^{t 1}, y^{t 1})}) (\frac{D_{o}^{t} (x^{t}, y^{t})}{D_{o}^{t 1} (x^{t}, y^{t})})]}^{0.5} \\ Efficiency Change Technological Cha n ge \end{matrix}

(8)

The first term on the right side of Equation (8) measures the contribution of technical efficiency change to productivity change. It takes a value greater than, equal to or smaller than one according to whether technical efficiency improves, remains unchanged or deteriorates, respectively, between periods t and t + 1. The second term on the right side of (8) measures the contribution of technological change to productivity change. It is the geometric mean of two terms, one comparing period t technology to period t + 1 technology from the perspective of period t data, and the other comparing the two technologies from the perspective of period t + 1 data.¹⁷ It takes the value greater than, equal to or less than one depending on whether technological progress, stagnation or regress has occurred, respectively, between periods t and t + 1.

The decomposition of Malmquist productivity change index into two productivity components, such as technical efficiency change and technological change is illustrated in Figure 1, where technical advance has occurred in the sense that $S^{t} S^{t 1}$ . Note that $(x^{t}, y^{t}) S^{t}$ and $(x^{t 1}, y^{t 1}) S^{t 1}$ , however, $(x^{t 1}, y^{t 1})$ does not belong to $S^{t}$ (i.e., technological progress has occurred).

In terms of the distances along the y axis, the index (8) becomes,

\begin{matrix} M_{o} (x^{t 1}, y^{t 1}, x^{t}, y^{t}) (\frac{od}{of}) (\frac{ob}{oa}) {[(\frac{od / oe}{od / of}) (\frac{oa / ob}{oa / oc})]}^{0.5} \\ (\frac{od}{of}) (\frac{ob}{oa}) {[(\frac{of}{oe}) (\frac{oc}{ob})]}^{0.5} \end{matrix}

(9)

The ratios inside the square bracket measure a shift in technology at input levels $x^{t}$ and $x^{t 1}$ , respectively, and so technological change is measured as the geometric mean of those two shifts. The term outside the bracket measures technical efficiency at t and t + 1, capturing changes in efficiency over time, that is, whether production is getting closer (catching up) or farther from the frontier.

We have calculated the output distance functions which make up the Malmquist productivity change index in Equation (8). The linear programming approach outlined by Fare et al. (1985) is utilised to compute the output distance functions and it is discussed in detail as follows. Assume that there are $k 1,, K$ observations using $n 1,, N$ inputs $x_{n}^{k, t}$ at each time period $t 1,, T$ . These inputs are used to produce $m 1,, M$ outputs $y_{m}^{k, t}$ .

The frontier (or reference) technology in period t is constructed from the data as,

\begin{matrix} S^{t} \{(x^{t}, y^{t}) : y_{m}^{t}_{k 1}^{K} z^{k, t} y_{m}^{k, t}, m 1,, M; x_{n}^{t}_{k 1}^{K} z^{k, t} x_{n}^{k, t}, \\ \frac{}{} n 1,, N; z^{k, t} 0, k 1,, K\} \end{matrix}

(10)

where $z^{k, t}$ is an intensity variable indicating at what intensity a particular activity (or observation) may be employed in production.¹⁸ This technology exhibits constant returns-to-scale (the intensity variable is constrained to be nonnegative)¹⁹ and a strong disposability of inputs and output.²⁰ It is important to point that the technology, and consequently the associated distance functions, is independent of the unit of measurement. The technology for period t + 1 is constructed similarly using data from period t + 1.

In order to calculate the productivity of observation k between t and t + 1 based on Equation (8), it is necessary to solve four different sets of linear programming problems. Here we make use of the fact that the output distance function is reciprocal to the output-based Farrell measure of technical efficiency and compute, for each k = 1, …, K,

\begin{matrix} {[D_{o}^{t} (x^{k, t}, y^{k, t})]}^{1} \max \\ subject to \\ y_{m}^{k, t}_{k 1}^{K} z^{k, t} y_{m}^{k, t}, m 1,, M \\ x_{n}^{k, t}_{k 1}^{K} z^{k, t} x_{n}^{k, t}, n 1,, N \\ z^{k, t} 0, k 1,, K \end{matrix}

(11)

The solution value of $*$ in (11) is equal to $* y^{k, t} / y^{k, t}$ , that is, the maximum potential output divided by the observed output. Accordingly, the output distance function is the ratio of the observed to the maximum potential output.²¹ This problem is also equivalent to the standard output-oriented DEA linear programming problem. Notice that as long as the observation being evaluated is a member of the reference set [the summed terms in (11)], the value of the distance function is less than or equal to unity for that observation. This must occur whenever the time superscript on $D_{o} (.)$ and the time superscript on the x and y argument in $D_{o} (x, y)$ are identical. The computation of $D_{o}^{t 1} (x^{k, t 1}, y^{k, t 1})$ is exactly like (11), where t + 1 is substituted for t. Since technology and the observation to be evaluated are from the same period, the solution value is less than or equal to unity.

Two of the distance functions used to construct the Malmquist index require information from two different periods, that is, the reference technology is constructed from data in one period, whereas the observation to be evaluated is from another period. One of these is computed from k as,

\begin{matrix} {[D_{o}^{t} (x^{k, t 1}, y^{k, t 1})]}^{1} \max \\ subject to \\ y_{m}^{k, t 1}_{k 1}^{K} z^{k, t} y_{m}^{k, t}, m 1,, M \\ x_{n}^{k, t 1}_{k 1}^{K} z^{k, t} x_{n}^{k, t}, n 1,, N \\ z^{k, t} 0, k 1,, K \end{matrix}

(12)

In (12), observations from periods t and t + 1 are involved. The reference technology relative to which $(x^{k, t 1}, y^{k, t 1})$ is evaluated is constructed from observations in period t.

In contrast in Equation (11), $(x^{k, t}, y^{k, t}) S^{t}$ , and therefore, $D_{o}^{t} (x^{k, t}, y^{k, t}) 1$ . However, in (12), $(x^{k, t 1}, y^{k, t 1})$ need not belong to $S^{t}$ , and so $D_{o}^{t} (x^{k, t 1}, y^{k, t 1})$ may take values greater than one.

The last linear programming problem required to construct the Malmquist index is also a mixed period problem. It is specified as in (12), but the t and t + 1 superscripts are reversed.

The (reciprocal of the) solutions to these linear programming problems may be substituted into Equation (8) to derive Malmquist productivity indexes for each observation for every adjacent pair of periods for which data are available.

5. DATA AND VARIABLES

5.1 Data

The main data source for the study is the Annual Survey of Industries (ASI) conducted by the Central Statistical Organisation of India. This data source provides information on unit-level or plant-level data for registered manufacturing industries. We used data on the textile machinery industry for the period from 1998–99 to 2007–08 at the 5-digit level of manufacturing industries classified according to National Industrial Classification (NIC) 1998 and 2004 from ASI.²² ASI data at the 5-digit level has rarely been analysed in any study on India.

5.2 Variables

Output: For the output variable we use the gross value added (GVA). The GVA of various 5-digit industries producing textile machinery are deflated by the wholesale price index (WPI) for textile machinery at 1993–94 prices. The WPI for textile machinery is obtained from the Office of the Economic Advisor, Ministry of Commerce and Industry.

Labour: We have used total man-days employed as the variable measuring labour input. Man-days represent the total number of days worked and the number of days paid for during the accounting year. It is obtained by aggregating the number of persons of specified categories attending each shift across all shifts worked on all days.

Capital: To calculate capital input, we deflated total fixed assets by the WPI for machinery and machine tools with the base year 1993–94. The measurement of capital input has been a controversial issue both in the theoretical and empirical literature. There is no universally accepted methodology for it, thus there are several methods for the estimation of capital input. In many studies, capital input is treated as a stock, measured by the book value of fixed assets (Kumar, 2006; Ray, 2002), while in others, it is considered a flow, measured by the sum of rent, repairs and depreciation expenses or perpetual inventory created from annual investment data (Ahluwalia, 1991; Balakrishnan and Pushpangadan, 1994; Trivedi, 2004). Each measure has its own shortcomings: the book value and perpetual inventory method do not address the question of capacity utilisation, whereas the flow measure may be questioned on the grounds that depreciation charges in financial accounts may be unrelated to actual depreciation of hardware (Kumar, 2006; Ray, 2002). To compute capital, we therefore, followed the procedure of Ray (2002) or Kumar (2006) in which total fixed assets are normalised by the appropriate price deflator. This method was also followed by Bhandari et al. (2010) and Kathuria et al. (2010, 2013).

6. EMPIRICAL ANALYSIS

For the empirical analysis of technological change in the textile machinery industry, we calculated TFP change and its multiplicative components—technological change (or innovation) and efficiency change in the textile machinery industry. The study sample looks at eight textile machinery industries over a period of 10 years, 1998–99 to 2007–08, thus giving us 80 sample units. Before we examine technological change, it is important to take a look at the gross value added of the textile machinery industry.

6.1 Gross Value Added of the Textile Machinery Industry

Table 1 presents the GVA of the textile machinery industry and of its major components over the period 1998–99 to 2007–08. It is apparent from the last column of the table that the GVA of the industry increased to ₹ 4.7 billion in 2007–08 from ₹ 1.8 billion in 1998–99, registered a growth of 6 per cent during this period.²³ The growth of the industry was mainly contributed by spinning machinery, whose GVA grew more than three-fold from ₹ 712 million in 1998–99 to ₹ 2.5 billion in 2007–08 (16 per cent growth). In contrast, parts and accessories of machinery, despite having a large share in the GVA of the industry, did not experience any growth.²⁴ Further, preparatory spinning machinery, weaving and allied machinery and processing and finishing machinery, despite having high growth rates (18.61 per cent, 15.7 per cent and 27.18 per cent, respectively), failed to push up the growth of the industry beyond 6 per cent due to their meagre stake in the industry. It is thus clear from the aforementioned discussion that the textile machinery industry was mostly affected by the behaviour of spinning machinery during the study period.

Table 1
Gross Value Added of the Textile Machinery Industry and its Major Components (at 1993–94 prices, ₹ million)

Year Preparatory Spinning Spinning Weaving & Allied Processing & Finishing Parts & Accessories Textile Machinery

1998–99 52.45 711.72 30.55 205.83 641.95 1,859.10

1999–00 831.00 515.78 28.17 1.86 1,258.94 3,984.04

2000–01 47.70 915.74 60.18 509.03 959.63 2,798.39

2001–02 29.19 632.49 107.24 461.26 817.72 2,931.67

2002–03 11.29 892.08 52.48 260.77 579.87 2,209.12

2003–04 129.19 1,187.19 86.90 12.63 579.23 2,913.80

2004–05 423.38 928.84 131.91 44.59 834.11 2,448.93

2005–06 218.51 1,500.79 71.53 175.35 930.97 3,007.22

2006–07 348.31 2,139.63 205.24 841.91 950.82 4,785.43

2007–08 316.71 2,546.42 75.71 801.32 845.90 4,697.79

Year	Preparatory Spinning	Spinning	Weaving & Allied	Processing & Finishing	Parts & Accessories	Textile Machinery
1998–99	52.45	711.72	30.55	205.83	641.95	1,859.10
1999–00	831.00	515.78	28.17	1.86	1,258.94	3,984.04
2000–01	47.70	915.74	60.18	509.03	959.63	2,798.39
2001–02	29.19	632.49	107.24	461.26	817.72	2,931.67
2002–03	11.29	892.08	52.48	260.77	579.87	2,209.12
2003–04	129.19	1,187.19	86.90	12.63	579.23	2,913.80
2004–05	423.38	928.84	131.91	44.59	834.11	2,448.93
2005–06	218.51	1,500.79	71.53	175.35	930.97	3,007.22
2006–07	348.31	2,139.63	205.24	841.91	950.82	4,785.43
2007–08	316.71	2,546.42	75.71	801.32	845.90	4,697.79

Source: Author’s computation based on ASI plant-level data.

6.2 Estimation of Technological Change in the Textile Machinery Industry

We calculated the Malmquist productivity index as well as technological change and efficiency change for each component of the textile machinery industry. It is known from section 4 that if the value of the Malmquist productivity change index or any of its components is less than one, this denotes a regress or deterioration in performance, whereas a value greater than one indicates improvements in the relevant performance.

Table 2 reports the annual estimation of the technological change, efficiency change and Malmquist productivity change index (TFP change) in the textile machinery industry. It is observed that technological change fluctuated between 1998–99 and 2007–08. There were some technological improvements along with technological declines in 2000–01, 2001–02, 2004–05 and 2007–08. The fluctuating technological change led to an unsteady TFP growth during the study period. Notwithstanding the fluctuations in technological change, on average, there was technological improvement—average technological growth was 3.2 per cent from 1999–2000 through 2007–08 (see the bottom row of Table 2).²⁵ This small technological innovation was the result of process innovation not product innovation, and was due to improvements in embodied technological changes brought about by the installation of new machinery or machines in production, which lead to improvements in the production process and innovations.²⁶ It is also observed that there was a little improvement in efficiency, evident from the average efficiency change in the textile machinery industry during the study period (see the bottom row of the Table 2), indicating a little catching-up process among the different components of the industry. It is important to note that the TFP growth (3.5 per cent) of the industry was largely due to growth in technological change.

Table 2
TFP Change and its Decomposition in the Textile Machinery Industry, Geometric Means (year-wise)

Year Efficiency Change Technological Change TFP Change

1999–2000 0.955 1.868 1.784

2000–01 0.710 0.877 0.623

2001–02 1.374 0.724 0.994

2002–03 0.899 1.100 0.989

2003–04 1.117 1.031 1.152

2004–05 1.261 0.698 0.880

2005–06 0.869 1.045 0.909

2006–07 0.544 3.115 1.696

2007–08 0.847 0.434 0.802

Mean 1.003 1.032 1.035

Year	Efficiency Change	Technological Change	TFP Change
1999–2000	0.955	1.868	1.784
2000–01	0.710	0.877	0.623
2001–02	1.374	0.724	0.994
2002–03	0.899	1.100	0.989
2003–04	1.117	1.031	1.152
2004–05	1.261	0.698	0.880
2005–06	0.869	1.045	0.909
2006–07	0.544	3.115	1.696
2007–08	0.847	0.434	0.802
Mean	1.003	1.032	1.035

Source: Author’s computations.

The component-industry-wise analysis of technological change in the textile machinery industry is given in Table 3. Apart from two (laundry-type washing and drying machines and dry-cleaning machines, and other machinery for textiles), all the other components of the industry experienced technological progress during the study period. The preparatory spinning machinery and the parts and accessories of machinery registered higher growth in innovations (11.6 per cent and 8 per cent, respectively), followed by the sewing and knitting machinery with 6.5 per cent, weaving and allied machinery with 5.7 per cent, spinning machinery with 2.8 per cent and processing and finishing machinery with 1.7 per cent during the period under consideration. Notwithstanding the high growth of innovation in most of the component-industries, the textile machinery industry failed to achieve much growth in process technological innovation. The textile machinery industry was, however, found to be mimicking the growth pattern of the spinning machinery. Spinning machinery—being an important segment of textile machinery industry—did not make much technological progress, and therefore, pulled down the overall technological innovation in the textile machinery industry.

Table 3

TFP Change and its Components in the Textile Machinery Industry 1998–99 to 2007–08

Component-Industries	Average Annual Change
Component-Industries	Efficiency Change	Technological Change	TFP Change
Preparatory spinning machinery	1.159	1.116	1.294
Spinning machinery	1.031	1.028	1.059
Weaving & allied machinery	0.971	1.057	1.026
Processing & finishing machinery	1.000	1.017	1.017
Sewing & knitting machinery	0.930	1.065	0.991
Laundry-type washing & drying machines & dry-cleaning machines	1.068	0.966	1.031
Parts & accessories of machinery	0.912	1.081	0.985
Other machinery for textiles	0.979	0.938	0.918
Mean	1.003	1.032	1.035

Source: Author’s computations.

6.3 User–Producer Interaction, Institutions and Innovation in the Textile Machinery Industry

Innovation is an interactive process and does not take place in isolation (Lundvall, 1988; Lundvall and Johnson, 1994). The meagre growth in innovation in the textile machinery industry is not the result of rational choice or effort of an individual decision making unit, but stems from interactive and communicative processes which take place in a (sectoral) system.²⁷ These interactive and communicative (or symbiotic) processes between users and producers play a very important role in generating innovations in a sector. Users’ demand for textile machinery symbolises the symbiotic processes between users and producers of the textile machinery industry. These symbiotic processes are, however, affected by institutions which are defined to be the set of habits, routines, norms, laws and policies that regulate or shape the symbiotic relationship amongst various agents and thus affect innovation processes and innovation (Johnson, 1992). In the present case, government policies on the textile machinery industry can influence the demand for domestic textile machinery and can thus affect user–producer interactions and innovations in the textile machinery industry. Let us see how textile policy affects user–producer interactions and eventually technological change in the industry.

Table 4
Demand for Textile Machinery in India (₹ million)

Year Production Minus Exports Imports Minus Imports of Parts and Accessories Total Domestic Demand Share of Domestic Producers in Total Domestic Demand

1993–94 10,250 10,240 20,490 0.50

1998–99 8,800 14,060 22,860 0.38

1999–00 8,670 10,990 19,660 0.44

2000–01 8,620 11,610 20,230 0.43

2001–02 6,450 12,320 18,770 0.34

2002–03 7,690 18,340 26,030 0.30

2003–04 806 2,179 2,985 0.27

2004–05 1,228 3,140 4,368 0.28

2005–06 1,730 6,768 8,498 0.20

2006–07 2,299 9,014 11,313 0.20

2007–08 (E) 2,524 7,049 9,573 0.26

Year	Production Minus Exports	Imports Minus Imports of Parts and Accessories	Total Domestic Demand	Share of Domestic Producers in Total Domestic Demand
1993–94	10,250	10,240	20,490	0.50
1998–99	8,800	14,060	22,860	0.38
1999–00	8,670	10,990	19,660	0.44
2000–01	8,620	11,610	20,230	0.43
2001–02	6,450	12,320	18,770	0.34
2002–03	7,690	18,340	26,030	0.30
2003–04	806	2,179	2,985	0.27
2004–05	1,228	3,140	4,368	0.28
2005–06	1,730	6,768	8,498	0.20
2006–07	2,299	9,014	11,313	0.20
2007–08 (E)	2,524	7,049	9,573	0.26

Source: Ministry of Textiles, Government of India.

Note: E—estimated.

Table 4 presents the demand behaviour of the textile machinery industry in India. It is seen that the total demand for textile machinery declined and fluctuated between 1998–99 and 2007–08. In 1998–99, total demand was ₹ 22.86 billion which increased to ₹ 26 billion in 2002–03, and again declined to ₹ 9.57 billion in 2007–08. Along with the poor and unsystematic demand for textile machinery, the share of domestic textile machinery manufacturers in total domestic demand declined sharply during the study period, to 26 per cent in 2007–08 from 44 per cent in 1999–2000. Further, imports had an increasing share in the total demand for textile machinery, most of it consisting of cheap second-hand or used textile machinery (Roy, 2010). The import of second-hand machinery was facilitated by the technology upgradation fund scheme (TUFS), which was initiated in 1999.²⁸ The TUFS—meant for providing interest reimbursement or capital subsidy to textile manufacturing—gives incentives to domestic textile producers to import second-hand machinery which increasingly takes away the demand share of domestic textile machinery producers. The declining demand for domestic textile machineries might have brought down the user–producer interactions, thereby bringing about poor process innovations in the textile machinery industry.

7. CONCLUSION AND POLICY SUGGESTIONS

The aim of the article was to measure technological change in the textile machinery industry and to see how the user–producer interaction is affecting the process of innovations in the industry. It was observed that there was a small degree of technological change in India’s textile machinery industry which resulted in a small amount of productivity growth in the industry, as the efficiency change had an insignificant share in productivity growth. It was also delineated that poor and unsustainable demand, along with the withering demand share of textile machinery producers owing to the TUFS, might have weakened user–producer interactions, thereby generating low levels of process innovations in the industry.

From our analysis of the textile machinery industry, it is clear that government policy with respect to textile manufacturing is affecting the production and innovation of textile machinery manufacturing, as it generates avenues for increasing imports, which in turn affect user–producer interactions and thus innovations in the textile machinery industry. It is unclear whether the TUFS has benefited textile manufacturing at all, but it has been detrimental to the textile machinery manufacturing. This policy will eventually make the country import-dependent, instead of self-reliant or self-sufficient. From the long-run economic perspective, the government should incentivise or insist that textile manufacturers buy or use machinery and equipment from domestic manufacturers of textile machinery, which will increase demand and strengthen user–producer interactions and thus innovations in textile machinery manufacturing, which in turn will again increase demand for textile machinery and also user–producer interactions and innovations. In this way, the virtuous cycle of innovation and diffusion will benefit both users and producers of the textile machinery industry in India. Through its current policy on TUFS, the government could insist users (textile manufacturers) buy machinery and equipment from domestic producers (textile machinery manufacturers) if they want to avail benefits under the TUFS; in this way, the government can establish the virtuous cycle of innovation and diffusion in the country.

Footnotes

Acknowledgements

The author is grateful to Professor Sunil Mani for his guidance and to the referee of the journal for valuable suggestions. This article has greatly benefitted from the helpful comments of Professor Amit Shovon Ray, Professor Pulapre Balakrishnan and Dr Ragupathy Venkatachalam, Centre for Development Studies, Trivandrum. The author is also grateful to Dr Anup Kumar Bhandari, Indian Institute of Technology (IIT), Chennai, for his suggestions.

APPENDIX 1

The details of the axioms of output distance function are discussed in Fare (1988: 31–34).

APPENDIX 2

Table A1

Classification of Textile Machinery Industry According to NIC-1998 and 2004

5-Digit Industries	NIC Code
Preparatory Spinning Machinery	29261
Spinning Machinery	29262
Weaving & Allied Machinery	29263
Processing & Finishing Machineries	29264
Sewing & Knitting machineries	29265
Laundry-type washing & drying machines & dry-cleaning machines	29267
Parts & Accessories of Machinery	29268
Other Machineries for textiles	29269

Source: NIC 1998.

Note: NIC-1998 division matches with NIC-2004 Division.

APPENDIX 3

Table A2

Average Annual Growth Rates: Output, Capital and Labour, 1998–99 to 2007–08

5-Digit Industries	Output	Capital	Labour
Preparatory Spinning Machinery	0.1861	0.1020	−0.0390
Spinning Machinery	0.1660	0.0576	0.0295
Weaving & Allied Machinery	0.1570	0.1612	0.1040
Processing & Finishing Machineries	0.2718	0.2084	0.2089
Sewing & Knitting machineries	−0.0060	0.0316	0.0964
Laundry-type washing & drying machines & dry-cleaning machines	−0.2174	−0.1937	−0.1851
Parts & Accessories of Machinery	0.0026	0.0738	0.0308
Other Machineries for textiles	−0.2344	−0.3106	−0.1688
Sample	0.0607	0.0183	0.0006

APPENDIX 4

Table A3

Share of Different Component-industries in Gross Value Added of Textile Machinery Industry

5-Digit Industry	1998–99	2002–03	2003–04	2004–05	2005–06	2006–07	2007–08	1998–99 to 2007–08
Preparatory spinning Machinery	2.82	0.51	4.43	17.29	7.27	7.28	6.74	6.99
Spinning machinery	38.28	40.38	40.74	37.93	49.91	44.71	54.20	37.34
Weaving & allied machinery	1.64	2.38	2.98	5.39	2.38	4.29	1.61	2.72
Processing & finishing machinery	11.07	11.80	0.43	1.82	5.83	17.59	17.06	9.96
Sewing & knitting machinery	0.65	3.32	2.36	2.27	2.08	1.31	0.90	2.67
Laundry-type washing & drying machines & dry-cleaning machines	2.25	7.14	1.15	0.22	0.58	3.62	0.05	2.34
Parts & accessories of machinery	34.53	26.25	19.88	34.06	30.96	19.87	18.01	27.73
Other machinery for textiles	8.75	8.22	28.02	1.02	1.00	1.33	1.43	10.25
Total	100	100	100	100	100	100	100	100

Source: Calculated using ASI plant-level data.

Technology Upgradation Fund Scheme (TUFS)

With a view to upgrade and modernise the Indian textile manufacturing, the Ministry of Textile launched the TUFS. The TUFS was first launched on 1 April 1999 for five years, and then extended to 31 March 2007. The scheme was further extended till 31 March 2012 and it has been approved to be continued during the 12th Plan period (2012–17). The detailed benefits and incentives of the TUFS are:

Reimbursement of 5 per cent (4 per cent in respect of new standalone/replacement/modernisation of spinning machinery) interest charged by the financial institutions or banks for technology upgradation projects.

Coverage of exchange rate fluctuation not exceeding 5 per cent (4 per cent in respect of spinning machinery) points per annum in respect of foreign currency loans instead of 5 per cent interest support.

An additional option to the powerlooms units to avail of 20 per cent margin money subsidy in lieu of 5 per cent interest reimbursement on investment on TUFS compatible specified machinery subject to a capital ceiling of ₹ 500 lakh and ceiling on subsidy ₹ 60 lakh.

15 per cent margin money for SSI textile and jute sector in lieu of 5 per cent interest reimbursement on investment in TUF compatible specified machinery subject to a capital ceiling of ₹ 500 lakh and ceiling on subsidy ₹ 45 lakh.

5 per cent interest reimbursement plus 10 per cent capital subsidy for specified processing machinery excluding Common Effluent Treatment Plant, garmenting machinery and machinery required in manufacture of technical textiles.

25 per cent capital subsidy on purchase of the new machinery and equipments for the pre-loom and post-loom operations, handlooms/upgradation of handlooms and testing and quality control equipments for handloom production units.

Interest subsidy/capital subsidy/Margin Money subsidy only on basis of the value of the machineries.

5 per cent interest subsidy or 25 per cent subsidy on benchmarked machinery at par with handloom sector.

Notes

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Measurement of Technological Change in India’s Textile Machinery Industry Using the Malmquist Productivity Index

Abstract

Keywords

1. INTRODUCTION

2. RELATED LITERATURE

3. THE TEXTILE MACHINERY INDUSTRY IN INDIA

3.1 Size and Distribution of the Industry 4

4. METHODOLOGY

4.1 Malmquist Productivity Index

5.1 Data

5.2 Variables

6. EMPIRICAL ANALYSIS

6.1 Gross Value Added of the Textile Machinery Industry

Footnotes

Acknowledgements

APPENDIX 1

APPENDIX 2

APPENDIX 3

APPENDIX 4

Technology Upgradation Fund Scheme (TUFS)

Notes

References

3.1 Size and Distribution of the Industry⁴