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Technical Change in India’s Rural Organised Manufacturing Industries

Nabanita Mitra (m.nabanita@alumni.iitg.ac.in) completed her PhD from the Department of Humanities and Social Sciences, IIT Guwahati, Guwahati. Debarshi Das (debarshidas@iitg.ac.in) teaches at the Department of Humanities and Social Sciences, IIT Guwahati, Guwahati.

Given the robust performance of India’s rural organised manufacturing industries in recent times, this study attempts to understand the nature of the technical change underlying it. Felipe and Kumar’s (2010) analytical framework is used to assess the direction of technical change in India’s rural industries in the period 1998–99 to 2016–17. The findings indicate that the direction of technical change was Hicks-neutral from 1998–99 to 2007–08 and Marx-biased from 2008–09 to 2016–17. At the disaggregated level, various industries exhibited diverse directions of technical change. The high growth seen in the economy in the first decade of this century was accompanied by a sustained rise in capital productivity. This ended subsequently, which contributed to a slowing down of the growth rate.

The authors would like to thank the anonymous referee for the suggestions on an earlier version of this paper. The authors are solely responsible for errors, if any.
 

The contribution of the rural manufacturing segment to India’s economy is growing. In 2011–12, it generated 51.3% of the economy’s manufacturing national domestic product (NDP), its output exceeding that of urban manufacturing (Chand et al 2017). The authors’ study (Mitra and Das 2019) on India’s rural organised manufacturing industries revealed that from 1998–99 to 2007–08, the real output of these industries registered a healthy growth rate. Although labour force growth trailed output growth, the fact that labour employment went up at a reasonable rate is encouraging. The productivity of rural industrial labour has risen along with a rise in capital productivity and capital per unit of labour. The rising capital per unit of labour indicates that, over the years, techniques of production have changed in India’s rural organised manufacturing industries. History shows that steady improvements in production technology with an increase in capital accumulation has resulted in higher productivity and economic growth (Foley and Michl 1999; Hayami and Godo 2005). This growth has sustained advancements in production technology through reinvestment of a part of the profit. The industrialisation that emerged in Britain in the late 18th century demonstrated the transformative power of technical change and has become a hallmark of capitalist economic growth (Foley and Michl 1999).

According to Marxist theory, profitability dictates the type of bias in technical changes in capitalist economies. Over the course of intense competition between capitals, as well as between labour and capital, the capitalist often gets biased towards labour-saving and capital-using technical change. While the output–labour ratio rises (hereafter referred to as “labour productivity”), the output–capital ratio falls (hereafter referred as “capital productivity”) as the capitalist reduces the cost of production at the prevailing level of real wages. Since the capitalist continues to sell their produce at prices decided by the higher costs of less technically advanced producers, their profits burgeon. The growth in net value added and profits go hand in hand with technical change and productivity improvements. This explains the capitalists’ bias (Foley and Michl 1999; Marquetti 2003). On the other hand, neoclassical economics sees the pattern of technical change as movements in the path of a “historically stable production function” (Marquetti 2003: 191). Critics disagree with Solow’s assumption that an aggregate production function captures the various possibilities of substituting capital for labour in a real economy. They argue that capital denotes the market value of various capital goods, whose prices could exhibit various patterns of change with a change in wages (Foley and Michl 1999).

A path whereby labour productivity rises and capital productivity falls matches with the classic Marxist template. But actual historical data may refuse to conform to this template. Marquetti (2003) analysed historical data on labour productivity and capital productivity to identify patterns of technical change in economies across the world. His findings indicate that the direction of technical change was not uniform over the course of economic development. Marx-biased technical change, that is, rising labour productivity and declining capital productivity, emerged as the dominant pattern, especially during periods of high growth, when there was an increased emphasis on mechanisation. However, in the post-1980s period, some regions of the world exhibited non-Marx-biased patterns of technical change.

Some studies have examined the direction of technical change in India’s organised manufacturing sector (Felipe and Kumar 2010; Basu and Das 2015). Felipe and Kumar (2010) analysed the direction of technical change in the organised manufacturing industry in India in 1980–2007 using a framework based on Sraffa’s (1960) real wage–profit rate schedule. The broad finding of their study was that the direction of technical change was Marx-biased till 2000 and thereafter Hicks-neutral (rising labour and capital productivity). Basu and Das’s (2015) study on profitability in India’s organised manufacturing sector found that it was Marx-biased from 1982–83 to 2001–02, while the period 2001–02 to 2012–13 was marked by a sharp rise in capital productivity as well as a more rapid rise in labour productivity. Felipe and Kumar (2010) observed that the pattern of technical change in India’s organised manufacturing industries differed from the trend seen in many other economies, in that capital accumulation in India did not pull down profit rates. Studies by Kannan and Raveendran (2009), Kapoor (2014), and Abraham and Sasikumar (2017) have examined the growth of capital intensity in India’s organised manufacturing sector in recent decades. Kannan and Raveendran (2009) observed that between 1983–84 and 1991–92, there was significant growth in labour and capital productivity across all industries in India’s organised manufacturing sector. The increase in labour productivity continued from 1992–93 to 2004–05, but capital productivity staggered (some industries registered an increase in capital productivity, whereas the rest showed a decline). The higher labour productivity in the 1990s over the preceding few decades has also been pointed out by Balakrishnan and Babu (2003). They found that labour productivity increased across all industries between 1991–92 and 1999–2000.

However, none of the studies so far have examined the nature of the technical change adopted in India’s rural industries. We want to address this gap, especially in the context of the growing salience of rural industries. From 1998–99 to 2016–17, rural industries surpassed urban industries in terms of output growth (net value added), employment, and profitability. In 1998–99, the real net value added and real profits of urban industries exceeded that of rural industries. In the succeeding periods, rural industries registered impressive growth rates, leading to higher levels of both net value added and real profits. These observations and the lack of literature on India’s rural industries motivated us to examine this segment. This paper tries to answer the following questions with regard to technical change in India’s rural industries: What have been the trends in labour productivity, capital productivity, and capital intensity in rural organised manufacturing industries in India from 1998–99 to 2016–17? How does one characterise the direction of technical change? In this way, the paper contributes to the small and growing body of literature mentioned above.

These questions are important not in the least because we are examining a particularly interesting period in this study. In India, there was steadfast growth in the real output of rural industries during the period 2002–03 to 2007–08 (Mitra and Das 2019). It roughly coincides with the period of unprecedented growth in the noughties decade in the Indian economy. Azad et al (2017: 85) described this period (2003–08) as a time of “visible acceleration of the real domestic product (GDP) growth rate” when investments financed by public sector bank credit surged. In this study, we try to understand what transpired in rural industries during this famous period as well as in the immediately succeeding period. Specifically, we propose to examine the direction of technical change during this time.

We examined rural industries with Industry Codes 15 to 36 (hereafter referred to as IC 15 and IC 36, respectively) for the period 1998–99 to 2007–08, both at the aggregate and disaggregated levels.1 We excluded IC 01 (agriculture), IC 14 (mining and quarrying), and IC 37 (recycling) and analysed data pertaining to manufacturing industries alone. We refer to this truncated set of industries as “rural industries” subsequently. We also examined data from all rural industries, at the aggregate level, for the period 1998–99 to 2016–17—which is a nine-year longer period than 1998–99 to 2007–08. The latter data set covers all industries; hence, a comparison of the two data sets is not feasible. We included it in our analysis as it gives more recent data on rural industries. It is referred to as “all rural industries” henceforth. The 1998–99 to 2016–17 data set does not have disaggregated data of individual industries; hence, the analysis will be limited in scope.

Data Sources Used

We used two sources of data in this study. The “Time-series Data on Annual Survey of Industries (1998–99 to 2007–08),” published by the Ministry of Statistics and Programme Implementation of the Central Statistics Office (CSO), provides nominal values of key characteristics of rural and urban industries in the organised sector at the aggregate and disaggregate levels. The nominal values of output, profits, capital stock,2 the wage bill, and the number of workers in industries IC 15 to IC 36 were used for this analysis (GOI 2011). A data series for a more extended period provided by the Annual Survey of Industries (ASI) is available as well (GOI nd). It provides aggregate level data for all rural and all urban industries for the period 1998–99 to 2016–17. As mentioned above, a shortcoming of the latter data set is that it does not provide disaggregated data.

In the next section, we present our empirical findings on the variations in labour productivity, capital productivity, and capital intensity of rural industries across the period 1998–99 to 2016–17. Following this, we present the analytical framework used by Felipe and Kumar (2010) to determine the direction of technical change. Next, we construct the real wage–profit rate schedules of rural industries to assess the direction of technical change at the aggregate level. To establish the robustness of our findings, we also compute the change in labour and capital productivity vis-à-vis Marquetti (2003) to assess the direction as well as the quantum of this change.

Labour, Capital Productivity and Capital Intensity

First, we present the labour productivity, capital productivity, and capital intensity trends3 of rural industries at both the aggregate and disaggregate levels during the period 1998–99 to 2007–08. This is followed by our findings on the same trends for all rural industries at the aggregate level, for 1998–99 to 2016–17. The analysis of the two sub-periods helps us understand whether data trends were maintained or differed in the latter sub-period, 2008–09 to 2016–17, vis-à-vis the earlier sub-period, 1998–99 to 2007–08. It is to be noted that the high growth phase of the economy ended around 2008. Hence, the division of the data at 2008 serves as an important marker to separate the entire period (1998–99 to 2016–17) into two parts.

Productivity changes from 1998–99 to 2007–08: Both, the labour productivity and capital productivity of rural industries, showed rising trends at the aggregate level in the period 1998–99 to 2007–08 (Figure 1). Labour productivity increased steadily from 2.74 in 1998–99 to 5.72 in 2007–08, barring mild dips in the period 2000–01 to 2001–02. Capital productivity levels showed a rise from 0.24 in 1998–99 to 0.49 in 2007–08. The findings by Kannan and Raveendran (2009) and Balakrishnan and Babu (2003) on productivity trends in India’s organised manufacturing industry present almost a similar picture. Capital intensity increased marginally from 11.64 in 1998–99 to 11.78 in 2007–08, although it showed decline and vacillation during the intermittent period (Figure 1). It was only in 2007–08 that capital intensity reached a level higher than the value reached in 1998–99. Although, at the aggregate level, capital intensity declined in some periods (1998–99 to 2000–01, 2001–02 to 2002–03, 2003–04 to 2004–05 and 2006–07), there was an overall increase by 1.4%.

On the other hand, at the aggregate level, capital productivity and labour productivity showed a spectacular increase of 104.17% and 108.85%, respectively. The observed increase in labour productivity at the aggregate level is largely attributed4 to improvements in capital productivity and, to a limited extent, to changes in capital intensity, as it exhibited only a small increase during the study period.

The above is true if all the industries are taken together. If they are considered at the individual level, a more heterogeneous picture emerges. At the disaggregated level, the labour productivity of all rural industries from IC 15 to IC 36 increased, indicating the adoption of labour-saving technologies (barring ICs 18 and 19). The leap in labour productivity was conspicuous in industries engaged in the production of tobacco products, cork, refined products and nuclear fuel, transport equipment, non-metallic mineral products, and basic metal (IC 16, 23, 26, and 27), and it was spectacular in IC 35—industries that manufacture other transport equipment (the output–labour ratio changed by 5.2 times). Labour productivity increased to a lesser degree in IC 25, which includes industries that manufacture rubber and plastic products, and it declined in IC 18 and IC 19, which include industries that manufacture wearing apparel and tanned leather, respectively.

Capital productivity increased in 15 out of 22 industries. In seven out of 22 industries, it declined, indicating that some industries adopted capital-using technologies. These were industries engaged in the manufacture of food products and beverages, tobacco products, wearing apparel, tanning of leather, computer machinery, communication equipment, and other transport equipment (ICs 15, 16, 18, 19, 30, 32, and 35). Gains in capital productivity were prominent in industries engaged in publishing, printing, and reproducing recorded media and in the manufacture of non-metallic mineral products and basic metals (ICs 22, 26, and 27). Thus, some rural industries adopted capital-using technologies, but most adopted capital-saving technologies. At the disaggregated level, capital intensity varied across industries. In 2007–08, capital intensity varied from 1.23 in the tobacco products manufacturing industry (IC 16) to 63.70 in the cork, refined petroleum products and nuclear fuel manufacturing industries (IC 23). The capital intensity of 11 out of 22 rural industries increased between 1998–99 and 2007–08. In Table 1 (p 51), rural industries are grouped as capital-intensive and labour-intensive based on their capital–labour ratio. An industry with a capital–labour ratio less than 5 () is considered as labour-intensive (LI) and that with a capital–labour ratio greater than 5 (≥ 5) is considered as capital-intensive (CI) in our classification. Based on this, we find that only six out of 22 rural industries are labour-intensive.

It is evident from Table 1 that the real net value added by labour-intensive industries is a small share of the total net value added by rural industries in total. The frequency of labour-intensive industries declines as we examine industries with a greater share in total output. This implies that the bulk of rural industrial output is produced by capital-intensive industries. In an earlier study, Panagariya (2008) discussed the low and stagnant share of labour-intensive products in Indian manufacturing. He holds this responsible for the inadequate generation of productive employment in the manufacturing segment. These results are akin to Kannan and Raveendran’s (2009) finding that in the period 1992–93 to 2004–05, the capital intensity of all industries in the organised manufacturing sector increased. Kapoor (2014) also found that in the period 2000–01 to 2010–11, the capital intensity of organised manufacturing industries increased in 19 out of 31 states/union territories. In our study, the increase was most pronounced in IC 35, which includes industries engaged in the manufacture of other transport equipment. This industry also registered a sharp change in labour productivity. The significant increase in capital intensity could have pushed up the labour productivity of IC 35.

In the next section, the above exercise is extended to all rural industries for the period 1998–99 to 2016–17, and thereafter for the sub-periods 1998–99 to 2007–08 and 2008–09 to 2016–17, separately.

Changes during 1998–99 to 2016–17: The labour productivity of all rural industries increased from 2.71 in 1998–99 to 5.48 in 2007–08 (Figure 2). Labour productivity exhibited a small fluctuation during 1998–99 to 2000–01 and then increased steadily to 5.66 in 2007–08. After 2008, barring some vacillations, it settled at 5.48 in 2016–17. The capital productivity of all rural industries increased from 0.24 in 1998–99 to 0.49 in 2007–08. Between 2008–09 and 2013–14, capital productivity wavered and eventually declined to 0.28 in 2016–17. Thus, during the entire period of 1997–98 to 2016–17, capital productivity rose first and declined later, forming an inverted U-shaped curve. Capital intensity increased from 11.49 in 1998–99 to 11.65 in 2007–08, with some intermittent fluctuation. In the next sub-period, 2008–09 to 2016–17, it increased steadily for all rural industries and reached the high level of 19.58 in 2016–17.

In the second sub-period (2008–09 to 2016–17), although the capital intensity of rural industries increased by 62.42%, compared to the increase of 1.29% in the previous sub-period (1998–99 to 2007–08), labour productivity merely increased by 1.36% (it exhibited a spectacular increase of 108.85% during the first sub-period). As for capital productivity, it declined by 37.59% during 2008–09 to 2016–17—a sharp reversal when compared with the rise in capital productivity (104.17%) during 1998–99 to 2007–08. As was the case with “rural industries,” for all rural industries, the change in labour productivity was primarily driven by changes in capital productivity rather than changes in capital intensity.5 Hence, declining capital productivity in the second sub-period led to a weak increase in labour productivity. We shall investigate the characterisation of this technical change later in this paper.

Frameworks to Assess Direction of Technical Change

Felipe and Kumar’s (2010) framework uses the real wage–profit rate schedule to arrive at the direction of technical change. The trade-off between wage rate and profit rate used in their formulation was provided by Sraffa (1960). The real wage–profit rate schedule is a downward sloping line, with the x axis denoting the real profit rate and the y axis the real wage rate (Figure 3). By using Sraffa’s real wage rate–profit rate schedule, the limitations of examining technical change using an aggregate production function are circumvented. Moreover, it is consistent with both neoclassical and heterodox models.

The nominal value of the net value added (Yn) gets distri­buted between the capitalists and the labourers as profits and wages, respectively.

Y= Wage bill + Profits

Yn = Wn + πn where Wn is the nominal value of the wage bill and πn is the nominal value of profits.

Yn = wn . L + r. Kn ... (1)

where L is the number of workers, wn is the nominal value of the wage rate, Kn is the nominal value of capital stock, and rn is the nominal value of the profit rate.

Considering a common deflator (the gross domestic product [GDP] deflator) for net value added, profit rate, capital stock, and wage rate, we obtain equation (2), that is, the real value of net value added as the sum of the real value of the wage bill and profits.

Y = w.L + r. K ... (2)

where is the real value of net value added, is the real wage rate, is the real profit rate, and K is the real value of capital stock.

The net value added per unit of labour is:

... (3)

 

where y is real labour productivity and is real capital productivity.

... (4)

Equation (4) depicts a relationship between the real wage rate and real profit rate in terms of labour productivity, that is, net value added per unit of labour (y) and capital productivity, that is, net value added per unit of capital (k). We obtain from equation (4) that the maximum real wage rate is equal to labour productivity when the real profit rate is zero (point m in Figure 3) and the maximum real profit rate is equal to capital productivity, when real wage rate is zero (point n in Figure 3). In Felipe and Kumar’s (2010) framework, a shift in the real wage–profit rate schedule implies a change in either or both the maximum wage rate (also the labour productivity) and maximum profit rate (also the capital productivity) due to a change in technique(s) in the production process. The slope of the real wage–profit rate schedule is the negative of the ratio of labour productivity to capital productivity (refer equation [5] below). The shift of the real wage–profit rate schedule and the resultant change in its slope indicates the direction of technical change.

... (5)

Technical change is termed as labour-saving when labour productivity is increasing, that is, ẏ > 0 and labour-using when labour productivity is decreasing, that is, ẏ < 0 . It is termed as capital-saving when capital productivity is increasing, that is, k. > 0 and capital-using when capital productivity is decreasing, that, k. < 0. Any technical change is a combination of these possibilities of labour and capital usage. The four categories of technical change, called Marx-biased, Harrod-neutral, Solow-neutral, and Hicks-neutral, are tabulated in Table 2.

In Marx-biased technical change, the real wage–profit rate schedule becomes steeper compared to the original position, the solid line in Figure 3. The Marx-biased change is indicated by i in Figure 3. In Harrod-neutral technical change, the y-intercept moves outward (labour productivity rises) and the x-intercept remains the same (capital productivity does not change). This corresponds to ii in Figure 3. In Solow-neutral technical change, the x-intercept moves outward (capital productivity rises) and the y-intercept remains the same (labour productivity does not change), corresponding to iii in Figure 3. In Hicks-neutral technical change, the real wage–profit rate schedule shifts outwards, parallel to the initial schedule, indicating that both labour and capital productivity are rising in tandem. This corresponds to iv in Figure 3.

In the next section, we examine the direction of technical change in India’s rural industries. For “rural industries,” we assess the direction of technical change both at the aggregate and disaggregate levels. For “all rural industries,” the same could only be assessed at the aggregate level.

Direction of Technical Change in Rural Organised Industries

The productivity trends in in the previous sections indicate the direction of technical change. A more precise assessment of the direction of technical change can be obtained through an examination of the changes in labour and capital productivity. We base our assessment on the methods used by Felipe and Kumar (2010) and Marquetti (2003). The second method was used to confirm the findings generated using Felipe and Kumar’s (2010) method. Felipe and Kumar’s methodology examines the shift(s) in the real wage–profit rate schedule(s) to find the change in x-intercept(s) (capital productivity) and y-intercept(s)
(labour productivity) to understand the direction of technical change. On the other hand, Marquetti (2003) computes the rate of change in labour and capital productivity, constructs scatter plots, and arrives at regional patterns of technical change in the world in the period 1964–90.

In the sections that follow, we first use Felipe and Kumar’s (2010) approach to arrive at the direction of technical change at the aggregate level.6 Given the large volume of data at the disaggregated level, it was not feasible to assess the direction of technical change of each industry using this method. At that level, we compute the rate of change in productivities to decipher the direction of technical change in line with Marquetti’s (2003) method.

Rural industries (1998–99 to 2007–08) at aggregate level: The real wage–profit rate schedules for the years 199899 and 2007–08 and the corresponding equations are given in Figure 4 and Table 3, respectively. The shift in the x-intercept from 1998–99 (i in Figure 4) to 2007–08 (ii in Figure 4) appears to be equal to the shift in the y-intercept during that period. In this period, the change in capital productivity was roughly equal to the change in labour productivity. Thus, for the period as a whole, the direction of technical change was Hicks-neutral. We also constructed the schedules for the intervening years (the lines between i and ii in Figure 4 correspond to each successive year) to gauge the consistency of the direction of technical change (refer Appendix Table A1 (p 57) for the underlying equations). The observed inter-year shifts in the real wage–profit rate schedules suggest that there was no uniformity in the nature of technical change during the intervening years.

 

In six out of nine periods, both labour productivity and capital productivity increased. This was termed as “input-augmenting technical change” by Marquetti (2003). Out of these six periods, three were relatively more labour-saving and the rest were relatively more capital-saving. In line with Marquetti’s study, the changes in labour productivity and capital productivity were also computed to confirm findings based on the movements of the real wage–profit rate schedule. The quantum of changes in capital productivity and labour productivity were 104.17% and 108.85%, respectively, as noted earlier. Thus, technical change was almost equally capital-saving and labour-saving. Findings on the individual industry-wise direction of technical change follow.

Rural industries (1998–99 to 2007–08) at disaggregate level: Based on Marquetti’s (2003) study, the rate of change of labour and capital productivity was used to assess the industry-wise direction of technical change. We infer from the computed values that seven of 22 industries (ICs 15, 16, 18, 19, 30, 32, 35) exhibited Marx-biased technical change. In the remaining 15 of 22 industries, both labour productivity as well as capital productivity exhibited increasing trends. In most of these industries (11 of 15), labour-saving tendencies were relatively more pronounced. Few (five of 15) industries were marked by rising productivities and a declining labour to capital ratio, implying that there was some substitution of labour by capital during this period. This pushed up labour productivity values. The seven of 22 industries that exhibited Marx-biased technical change (declining capital productivity and rising labour productivity), were light industries that registered positive growth in capital intensity. This indicates that capital deepening boosted labour efficiency, and capital continued to become less productive. This is especially true for IC 35 (manufacture of other transport equipment).

All rural industries (1998–99 to 2016–17) at aggregate level: This section examines whether all rural industries exhibited technical change similar to “rural industries” and whether the direction of technical change was sustained post 2007–08. Figure 5 (p 54) gives the real wage–profit rate schedules of all rural industries for the period 1998–99 to 2016–17. Lines i, ii, and iii correspond to the real wage–profit rate schedule in the years 1998–99, 2007–08, and 2016–17, respectively.

In the aforementioned period, 1998–99 to 2016–17, the change in labour productivity was 102.21% and capital productivity was a mere 16.67% at the aggregate level, indicating that technical change was prominently labour-saving and a trifle capital-saving in all rural industries. The direction of technical change tended more towards Harrod-neutral (where labour productivity rises but capital productivity does not) rather than Hicks-neutral and can simply be looked upon as “input-augmenting technical change.” To get a more nuanced view, we construct separate real wage–profit rate schedules of all rural industries for the sub-periods 1998–99 to 2007–08 and 2008–09 to 2016–17.

Sub-period 1998–99 to 2007–08: Figure 6 shows that in the period 1998–99 to 2007–08, there were almost similar outward shifts in the x-intercept (capital productivity) as well as in the y-intercept (labour productivity) for all rural industries. The changes in labour and capital productivity were 108.86% and 104.17%, respectively. This reveals that technical change was marginally more labour-saving than capital-saving and tended to be Hicks-neutral in character in the first sub-period. Thus, the direction of technical change of all rural industries was identical to rural industries during 1998–99 to 2007–08, which we discussed above. Note, these lines are not to be confused with the similar looking lines in Figure 4. In Figure 4, a subset of all rural industries was considered, whereas, in Figure 6, we consider all rural industries. As the results are broadly similar, one can conclude that what was true for the subset of industries holds true for the entire set.

Sub-period 2008–09 to 2016–17: Figure 7 shows that in the period 2008–09 to 2016–17, the x-intercept (capital productivity) moved inwards and the y-intercept (labour productivity) moved slightly outwards. Thus, the direction of technical change was distinctly different from that in the previous sub-period. It was a trifle labour-saving and at the same time capital-using. This can be classified as Marx-biased technical change. Labour productivity rose by about 1.29% and capital productivity declined by 38%.

The key observations from the discussed evidence are: (i) In the period 1998–99 to 2007–08, “rural industries” as well as all rural industries exhibited Hicks-neutral technical change at the aggregate level: both labour and capital productivities rose. (ii) The direction of technical change of all rural industries switched to Marx-biased in the period 2008–09 to 2016–17: labour productivity rose but capital productivity fell. (iii) At the disaggregated level, industries IC 15 to IC 36 showed diverse directions of technical change during 1998–99 to 2007–08—most exhibited an increase in labour productivity while capital productivity fell in some and rose in others.

To summarise, our analysis provides an understanding of productivity and capital intensity trends and the direction of technical change in India’s rural organised manufacturing industries over the two sub-periods of 1998–99 to 2007–08 and 2008–09 to 2016–17. The pronounced rise in labour productivity in the first sub-period aligns with findings on labour productivity trends observed in India’s organised manufacturing segment by Balakrishnan and Babu (2003) for the period 1991–92 to 1999–2000, Kannan and Raveendran (2009) for the sub-periods 1983–84 to 1991–92 and 1992–93 to 2004–05, Felipe and Kumar (2010) for the period 1980–2007, and Basu and Das (2018) for the period 1982–2012. As there are no studies on India’s organised manufacturing after 2012, no comparisons could be drawn with the muted rise in labour productivity during 2008–09 to 2016–17. In our study, capital productivity registered a healthy rise in the period 1998–99 to 2007–08 and a decline after 2008–09. The fall in capital productivity is not unprecedented. Kannan and Raveendran (2009) also observed a decline in capital productivity in India’s organised manufacturing segment in the period of 1992–93 to 2004–05. We observe a small change in capital intensity in the first sub-period and a big jump in capital intensity in the second sub-period. The rising capital intensity in the 1990s and the 2000s in India’s organised manufacturing segment is confirmed by Kannan and Raveendran (2009) and Kapoor (2014).

Regarding the direction of technical change, from the above discussion, one is led to the conclusion that in the first sub-period (1998–99 to 2007–08), it was Hicks-neutral, whereas in the second sub-period (2008–09 to 2016–17), it was Marx-biased. Felipe and Kumar (2010) and Basu and Das (2015) found that the direction of technical change was Marx-biased in the periods 1980 to 2000 and 1982–83 to 2001–02, respectively, in India’s organised manufacturing industries. Felipe and Kumar (2010) found that from 2000 to 2007, the direction of technical change was Hicks-neutral. If all these are put together, the overall trend appears to be Marx-biased technical change—of declining capital productivity and rising labour productivity. There was an interim period in the first decade of this century when both capital and labour productivity rose. That period has ended, at least in the rural industrial sector. The productivity of capital has been declining in the recent phase.

Conclusions

Our findings indicate that the first phase (1998–99 to 2007–08) of growth in rural industries was driven by rising labour productivity and capital productivity. Capital intensity did not change much. After 2007–08, however, the pattern of technical change altered dramatically. Labour productivity rose mildly, while capital productivity fell sharply. Hayami and Godo (2005) termed the latter trend as the Marx pattern of growth. It’s characterised as Marx-biased technical change in the heterodox economics literature. When capital productivity falls, the rate of profit ceteris paribus declines. It is not surprising, therefore, that one sees a decline in growth after 2007–08. There was a short spurt lasting two years during 2009–10 to 2011–12. Unfortunately, it was short-lived since the decline in capital productivity was relentless in the entire period of 2007–08 to 2016–17.

A number of studies, including by Azad et al (2017), have delved into the reasons for the high growth in India during the 2000s. Our paper, while focusing on rural organised industries, offers some clues in this regard. Till 2007–08, technical change was Hicks-neutral, while both labour and capital productivity rose. This was a period of fast growth: rising capital productivity ensured a healthy and rising profit rate. This observation is consistent with the results of Basu and Das (2018), who, unlike us, took both rural and urban organised industries together and found quickly rising capital productivity until 2007. After 2007–08, the character of rural industries’ technical change underwent a shift. Capital productivity slid downwards, while labour productivity rose only marginally. Not surprisingly, the growth rate suffered as the 2000s ended. Needless to say, the issues that Azad et al (2017) have flagged to explain the high growth and its slowing down are important. The argument that the global financial crisis precipitated the slowing down of growth in India also has merit (Ghosh and Chandrasekhar 2009). Our modest submission is that probing technical changes may throw additional light on the shifting growth patterns.

This paper did not pay attention to the demand side, for our focus has been on technical change. Favourable demand conditions are assisted by mass purchasing power. In this context, the message of the paper—that technical change has been Marx-biased, and capital intensity has been rising—is bad news. It implies that from the supply side, output is driven more by capital than labour. Limited labour employment implies limited purchasing power, implying flat demand. A policy implication that follows from this is that mass purchasing power has to be kindled. Many tools are available to achieve this—expansion of the scope of rural employment generation schemes, the public distribution system (PDS), public health services, etc. A number of researchers, Kotwal and Sen (2019), for instance, do agree with such a prescription of stimulating demand. The COVID-19-induced deceleration has only underlined the salience of this kind of economic strategy.

Notes

1 See all industry codes in Appendix, Table A2 (p 57).

2 Capital represents the depreciated value of fixed assets owned by the factory as on the closing day of the accounting year (GoI 2011).

3 Detailed data not reported here; can be provided on request.

4 Differentiating the identity we get. In other words, growth rate in labour productivity is the summation of the growth in capital productivity and capital intensity.

5 See note 2.

6 To validate the findings from Felipe and Kumar’s (2010) framework, we calculate the change in labour and capital productivity according to Marquetti’s (2003) method.

References

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Updated On : 20th Oct, 2020

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