CHAPTER-V PRODUCTIVITY IN INDIAN MANUFACTURING SECTOR: PRE- AND POST-REFORM COMPARISON

Transcription

1 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 revelation 33 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). 119

2 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, ). Secondly, the controversy also looms on the choice of inputs (output or value added; net or gross 35 ; 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. 120

3 two inputs (Goldar, 1986) verses three inputs (Pradhan and Barik, 1998)). Further, the measurement of capital stock is most controversial 36 (Trivedi et al., 2000) and so does the rate of its discarding 37. 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 postreform 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,

4 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 postreform 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. 122

5 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 Office, Accounting & computer Electrical valves & tubes TV & Radio transmitters TV & Radio receivers Medical appliances Optical instruments Watches and clocks Aircrafts and Spacecrafts High Technology Industries Medium- High Technology Industries 241 Basic chemicals * Other chemical products Manmade fibers General purpose machinery Special purpose machinery Domestic appliances Electronic motors etc Electricity distribution & control app Insulated wires & cables Accumulators, cells etc Electronic lamps etc Other electrical equipment Motor vehicles Bodies for motor vehicles Parts for vehicles Railways and tramways etc Transport equipment n.e.c Medium- High Technology Industries Medium- Low Technology Industries 231 Coke-oven products Refined petroleum products

6 233 Process of nuclear fuel Rubber products Plastic products Glass & glass products Non-metallic minerals Basic Iron ore & steel Basic & non-ferrous metal Casting of metals Structural metal etc Fabricated metal etc Building & repair of ships Medium- Low Technology Industries Low Technology Industries 151 Production & process of meat Dairy products Grain mill products Other food products Beverages Tobacco products Spin, weaving of textiles Other textiles Knitted & crochet fabrics Wearing apparel, not fur Dressing & dyeing of fur Leather Footwear Saw milling of wood Wood, corks & straw Paper & paper products Publishing Printing Reproduction of recorded media Furnishing Manufacturing n.e.c. jewellery Low Technology Industries Total Organized Manufacturing Industries Notes: Refer Appendix I for industry names. Data source: EPWRF (2004), ASI (CSO) 2004;

7 Amongst the LT industries, the food products ( ) showed throughout a very low trend growth rate in both the sub-periods. Also, disappointing was the trend growth rate of textile industries ( ), 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;

8 Thus, the overall picture (Figure 5.1) shows that only 19.6 per cent, that is 10 out of 51 industries 38 accelerated in the post-reform period as compared to the prereform 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; 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 postreform 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. 126

9 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 TFPG decelerated in post-reforms Das (2004) Organized manufacturing Industries Gross Output Function TFPG decelerated in post-reforms Ahluwalia (2006) Organized manufacturing Industries Single Deflation TFPG decelerated in post-reforms Goldar (2006) Organized manufacturing Industries Double Deflation Gross output function TFPG decelerated in post-reforms Present Study Organized manufacturing Industries Single Deflation 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 127

10 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). 128

11 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 129

12 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 manufacturing industries for the period to as well as for the pre-reform period and the post-reform period are presented in Table 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. 130

13 Table 5.3. Independent Variables Production Function Constant ( β 0) 1.36 (1.36) Log Labour ( β 1) Log Capital ( β 2) Stochastic Frontier Production Function Estimates Dependent Variable: Net Value Added Organized Industries High Technology Industries Medium-High Tech Medium-Low tech Low-Technology Industries Prereform Post Pre- Post Pre- Post Pre- Post Pre- Post Reform reform Reform 06 reform Reform 06 reform Reform 06 reform Reform 0.59 (0.83) 0.35 (0.5) Year ( β 3) (-0.15) Inefficiency Model Constant 0.02 (δ 0 ) (0.03) t (δ 1 ) 0.04 (0.37) t 2 (δ 2 ) (-1.07) σ *** (9.47) γ 0.07 *** Loglikelihood No. of. observations Notes: (5.8) (0.43) 0.43 (0.59) 0.54 (0.92) (-0.1) (0.002) 0.06 (0.14) (-0.43) 0.19 *** (7.4) 0.05 *** (2.7) *** (2.86) 0.51 *** (8.41) 0.44 *** (7.59) -0.1 (-1.31) -0.5 *** (-4.07) 0.04 *** (2.65) ** (-2.4) 0.22 *** (13.3) (1.04) *** (5.39) 0.89 *** (10.5) 0.02 (0.2) 0.07 *** (2.98) (-1.4) 0.9 (1.30) (-1.1) 0.37 *** (3.36) 0.79 *** (8.2) (-1.1) 0.32 *** (4.43) 0.71 *** (9.03) 0.01 (0.87) (-0.2) 1.22 (0.18) (-0.18) 2.23 (0.19) 0.96 *** (6.42) *** (4.5) 0.66 *** (14.2) 0.3 *** (6.63) 0.02 *** (3.2) -7.9 (-1.5) 0.77 * (1.8) * (-1.9) 0.85 * (1.83) 0.89 *** (13.0) *** (5.3) 0.61 *** (7.92) 0.32 *** (4.6) (-1.04) (-0.66) 0.74 (0.92) (-0.91) 0.73 (0.85) 0.96 *** (19.1) ** (1.98) 0.52 *** (11.7) 0.45 *** (11.9) 0.04 *** (4.4) -4.8 * (-1.8) 0.96 ** (2.06) ** (-1.99) 0.27 ** (2.5) 0.84 *** (9.68) 0.97 *** (4.6) 0.69 *** (17.3) 0.28 *** (8.4) 0.01 *** (2.8) (-0.30) 0.26 (0.34) (-0.35) 2.00 (0.35) 0.97 *** (14.0) 3.28 *** (4.51) 0.29 ** (2.13) 0.48 *** (4.8) (-1.5) (-1.31) 0.43 ** (2.17) ** (-2.23) 0.5 *** (9.5) 0.07 (0.91) 3.12 *** (6.15) *** (-3.5) 0.97 *** (14.6) (-0.3) *** (-5.07) 0.23 *** (4.6) *** (-4.64) 0.25 *** (7.8) 0.02 *** (7.8) 3.17 *** (6.9) (-0.12) 0.73 *** (23.8) *** (-8.2) *** (-4.4) 0.07 *** (4.8) *** (-4.1) 0.38 *** (11.9) 0.00 (0.5) 1.41 *** (10.2) 0.75 *** (21.3) 0.19 *** (6.45) 0.06 (0.95) (-0.66) -1.1 (-0.84) 0.08 (0.85) 0.53 (0.94) 0.89 *** (7.5) 0.63 ** (2.4) 0.51 *** (10.7) 0.45 *** (11.2) (0.09) -0.3 * (-1.76) 0.06 *** (2.71) *** (-3.7) 0.14 *** (11.6) 0.06 (0.88) 1.21 *** (8.66) 0.61 *** (17.5) 0.33 *** (11.8) ** (-2.3) (-0.2) 0.02 ** (1.96) *** (-3.24) 0.11 *** (14.1) 0.02 *** (8.14) *, ** 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), and

14 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 ( to ). 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 subgroup 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 subgroup. 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 valueaddition factor for the period under study. The time variable coefficient (β 3 ) 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 (δ 1 ) 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. 132

15 The remaining two parameters ζ 2 and γ = ζ 2 u/(ζ 2 u + ζ 2 v) are associated with the variance of the random variables v it and u it ; 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(H 0 )/L(H 1 )]...(1) where L(H 0 ) and L(H 1 ) are the value of the likelihood function for the frontiers models under the null and alternative hypothesis, H 0 and H 1, 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 H 0 : γ = 0 Organized Manufacturing ( ) H 0 :γ=δ 0 = δ 1 =0 HT MHT MLT LT Organized Manufacturing LR chi2 Decision 4.39 ** Reject H 0 Pre-Reform ** Reject H 0 Post-Reform ** Reject H *** Reject H 0 Pre-Reform *** Reject H 0 Post-Reform *** Reject H *** Reject H 0 Pre-Reform ** Reject H 0 Post-Reform ** Reject H *** Reject H 0 Pre-Reform 2.23 Post-Reform *** Reject H 0 Pre-Reform 6.67 Post-Reform *** Reject H *** Reject H 0 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 and for the 5 percent and 1 percent level of significance. 133

16 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 to 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 Pre-Reform Post-Reform Change in TE # HT *** MHT ** MLT LT Organized Manufacturing 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. 134

17 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 High Technology Industries Industry to Pre-Reform ( to ) Post-Reform ( to ) Change # 2423 Pharmaceutical Office, Accounting & computer Electrical valves & tubes TV & Radio transmitters *** 323 TV & Radio receivers Medical appliances *** 332 Optical instruments Watches and clocks *** 353 Aircrafts and Spacecrafts *** Medium- High Technology Industries 241 Basic chemicals Other Chemical products General purpose machinery Special purpose machinery Domestic appliances Electronic motors etc ** 312 Electricity distribution & control app *** 313 Insulated wires & cables Accumulators, cells etc

18 341 Motor vehicles Railways and tramways etc Transport equipment n.e.c *** Medium- Low Technology Industries 231 Coke-oven products Refined petroleum products Rubber products Plastic products Glass & glass products Non-metallic minerals Basic Iron ore & steel Basic & non-ferrous metal Structural metal etc Fabricated metal etc Building & repair of ships Low Technology Industries 151 Production & process of meat Dairy products Grain mill products Other food products Beverages Tobacco products Spin, weaving of textiles Other textiles Knitted & crochet fabrics Wearing apparel, not fur Leather Footwear Saw milling of wood Wood, corks & straw Paper & paper products Publishing Printing Furnishing Manufacturing n.e.c. jewellery 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. 136

19 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 prereform 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 postreform 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

20 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 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 outwardoriented policies as the only means of enhancing efficiency would not be an 138

21 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. 139