LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA: A STATE-WISE ANALYSIS

The Indian Journal of Labour Economics, Vol. 49, No. 3, 2006 LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA: A STATE-WISE ANALYSIS R.K. Sharma and Abinash Dash* Based on the latest available NSS (56th round) and ASI data for 2000-01, this paper examines the structure and composition of Small Scale Industry (SSI) and the productivity differences between the small and large scale enterprises. The paper also explores the correlates of labour productivity in the SSI sector. Morever, it discovers strong inter-linkages between SSI and large scale manufacturing. Labour productivity in the unorganised manufacturing sector is seen to have an inverse relationship with poverty; implying that measures to raise labour productivity can bring about poverty reduction. Therefore, there is a need for capital investment and technological upgradation in the SSI sector. Analysis reveals that a large number of enterprises in this sector are technologically backward and a substantial number of workers underemployed. Availability of credit has been found to have significant positive impact on labour productivity, especially in urban areas. The paper also finds that the existence of sub-contracting phenomenon does not have much impact on labour productivity; and therefore it is only a short-term measure to raise employment and number of enterprises. Hence, a sustainable level of employment and productivity could be achieved if the state initiates policies to provide social security, marketing facility, technological upgradation, training and skills to workers and above all the infrastructural support to the millions of tiny enterprises in the SSI sector.. I. INTRODUCTION The Small Scale Industries (SSI) sector has always been an important area of concern for the planners because of the prominent place it has acquired in the socio-economic development of the country in the last five decades. It has contributed to the overall growth of the gross domestic product as well as employment generation and export. Performance of the small-scale sector, which forms a significant part of the industrial sector, has a direct impact on the growth of the national economy. The manufacturing sector in India (of which SSI is a major component) has a vital role to play in the overall economic development of the country. Table 1 shows that percentage share of manufacturing sector in terms of employment has varied between 11 to 12 per cent from 1983-84 to 1999-2000 whereas its contribution to GDP has steadily increased over the years from 14.9 per cent in 1983-84 to 16.8 per cent in 1999-2000 at 1993-94 prices (GoI, 2004). But, increase in jobs in the manufacturing sector has not kept pace with the overall growth in the sector which raises some concern. There is a need to devise some strategies, which can reverse the slowdown in growth of employment in the manufacturing sector. Such a strategy becomes more relevant at a time when one of the main challenges before the country is the problem of unemployment, particularly disguised unemployment. Sandesara (1988) suggests * Professor and Research Scholar, respectively, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi. Comments and suggestions by an anonymous referee of the Journal and P.P. Sahu were extremely useful.

408 THE INDIAN JOURNAL OF LABOUR ECONOMICS that for increasing employment, modern small scale industries based in medium-sized towns should be promoted. Table 1 Percentage Share of Manufacturing Sector in Employment and Gross Domestic Product Year Percentage share in employment Percentage share in GDP 1983-84 11.6 14.9 1987-88 11.9 16.1 1993-94 11.1 16.1 1999-2000 12.1 16.8 Source: GoI, National Accounts Statistics and Economic Survey (Various Issues). It is because of the role of SSI in employment generation and rural development that this sector has continued to attract attention of the policy makers from the heydays of planning. The role of SSI has been production focused because of the policy makers pre-occupation with employment generation. In the era of liberalisation and WTO regime, it is the quality production and efficiency rather than mass production that matters. Promotional and protection oriented measures regarding the SSI have always targeted augmenting employment regardless of productivity of the workers. In terms of industrial safety, pollution control and labour laws, the unorganised sector is doing miserably as compared to the organised sector. The country s industrialisation process, however, still has a long way to go before the budding entrepreneurs of today get transformed into big industrialists of tomorrow. Does this mean that there is an employment-productivity trade off? The answer is no. If the underemployed and disguised unemployed in agriculture sector are taken care of by providing them adequate training, credit facility, latest technology and better environment to work, then productivity and employment can grow simultaneously. In the backdrop of the above discussion, this study reflects on some of the issues relating to labour productivity in the unorganised manufacturing sector. Huge size coupled with impressive growth in this sector is clear from Table 2. In the year 2000-01, 34 lakh units were engaged in SSI activity, generating employment for 186 lakh workers and contributing Rs. 60,000 crore to exports. Contribution of SSI in all these dimensions has steadily increased. Number of units has increased from 19 lakh units in 1990-91 to 34 lakh units in 2000-01, thus registering an average annual compound growth rate of around 6 per cent per annum. On the employment front, while it was generating employment for 125 lakh workers in 1990-91, it increased to 186 lakh workers in 2000-01. Its contribution to exports increased from Rs. 10,000 crore to Rs. 60,000 crore during the period 1990-91 to 2000-01. Further, the SSI s share in national value added and exports is 39 per cent and 34 per cent respectively. SSI units in India produce over 8000 items and 675 items have been reserved for them, in which they can undertake production exclusively. Having a glance at the performance, contribution, size and growth of the SSI is enough to reassure the attention it deserves. But the wide range of availability of data with varying definitions makes a meaningful study difficult, though not impossible. The small scale industries are wide ranging in production lines and are also spread across vast geographical area. Thus, it has caused problems for the enumerators to enlist each one of them. According to the definition, the SSI is spread across both the organised and unorganised manufacturing. There is no criterion available on the basis of which the ASI data can be meaningfully classified to sieve the SSI part from it. Data for small scale industries in India is collected by a number of Institutions/Ministries.

LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 409 Table 2 Performance of Small Scale Industrial Sector Production (in Rs crores) Year No. of Units At current At 1970-71 Employment Exports (in lakhs) price) price) (in lakhs) (in Rs crores) 1990-91 19.48 31160 31160 125.3 9664 1991-92 20.82 178699 32129 129.8 13883 (6.88) (3.1) (3.59) (43.65) 1992-93 22.46 209300 33935 134.06 17784 (7.88) (5.6) (3.59) (28.11) 1993-94 23.88 241648 36948 139.38 25307 (6.32) (7.1) (3.97) (42.29) 1994-95 25.71 298886 37362 146.56 29068 (7.66) (10.1) (5.15) (14.86) 1995-96 26.58 362656 41621 152.61 36470 (3.38) (11.4) (4.13) (25.46) 1996-97 28.03 411858 46333 160 39248 (5.46) (11.32) (4.84) (7.61) 1997-98 29.44 462641 50239 167.2 44442 (5.03) (8.43) (4.5) (13.23) 1998-99 30.8 520650 54108 171.58 48979 (4.62) (7.7) (2.62) (10.21) 1999-00 32.12 572887 (8.16) (4.03) (10.66) (4.29) 58523 178.5 54200 $ 2000-01(P) 33.7 645496** 63258 185.64 59978 (4.92) (8.09) (4.0) (10.66) Note: (P) for Provisional; Figures in the parentheses give the percentage increase over the previous year; $= Revised; ** = figures are on the price at the end of January 2001. Source: Handbook of Industrial Policy and Statistics (2000-01). Small Industrial Development Organisation (SIDO) and National Sample Survey Organisation (NSSO) have provided estimates of key parameters at different points of time with varying time gaps. The data provided by SIDO suffers from serious methodological problems. Saluja (2004) points out that there are a number of limitations in the data produced by the Development Commissioner Small Scale Industries (DC-SSI). Firstly, the annual data on key parameters like number of units, production and employment at all India level may not be realistic as it is based on a mere two per cent sample of working units. Secondly, the investment limit on plant and machinery to classify an industry has undergone constant upward revision, which makes inter-temporal comparison difficult. And finally, the mortality rate of SSI is very high. Due to these problems it is difficult to rely on DC-SSI data. It is, however, plausible to argue that the unorganised sector is a better representative of SSI. There are two problems in such an argument. Firstly, some DME 1 enterprises may not come under SSI and secondly, NSSO does not classify enterprises on the basis of investment in plant and machinery. But the number of enterprises in DME sector, which may not fall under SSI, would be small as the number of enterprises in this sector itself is very low as compared to the number of enterprises in the entire unorganised manufacturing. As far as classifying SSI on the basis of investment in plant and machinery is concerned, even the DC-SSI (as mentioned above) had been altering the investment level very often for definitional purpose of SSI. Therefore, this study takes the unorganised manufacturing as a representative for SSI and the organised sector represents medium and large scale enterprises. Throughout the paper the Unorganised Manufacturing Sector (UMS) will be

410 THE INDIAN JOURNAL OF LABOUR ECONOMICS used as synonym for SSI and Organised Manufacturing Sector (OMS) for large scale industries. For the organised sector the data has been collected from the ASI (Summary Results) Factory Sector (2000-01) and that of unorganised from the NSSO (56th Round, 2000-01) at current prices. The study also makes use of SIDO, National Accounts Statistics (NAS) and Economic Survey data to supplement the data of ASI and NSSO. The study attempts to present a state level analysis of SSI in terms of its structure and composition in rural and urban areas separately and for different categories of enterprises (i.e. OAME, NDME, and DME). Further, the paper works out various structural ratios at state level, which provide insight into the state of affairs of the SSI. These structural ratios are labour productivity (O/L), capital productivity (O/K), and capital-labour ratio (K/L). The paper intends to decipher the factors affecting the labour productivity and works out relative productivities, thereby, helping to compare the efficiency of small and large scale industries. The states of Bihar, Madhya Pradesh, and Uttar Pradesh are taken as undivided, that is they include Jharkhand, Chattisgarh, and Uttaranchal respectively. The paper is divided into seven sections. The second section is devoted to the structure of the SSI sector in the industrial sector as a whole; the third section presents the composition of the SSI sector vis-à-vis the large and medium scale sector; the fourth section deliberates on state-wise analysis of labour productivity vis-à-vis the large and medium scale sector; the fifth section explores the existence of inter-linkages between the two sectors and that of labour productivity and poverty; the sixth section tries to find out the factors affecting the labour productivity in the SSI sector as a whole and in rural and urban areas separately; and finally, the seventh section summarizes the findings of the previous sections and discusses some policy options. II. STRUCTURE OF THE UNORGANISED MANUFACTURING SECTOR This section deals with the structure of the unorganised manufacturing sector in India. It looks at the share of the unorganised manufacturing sector (UMS) in the Indian manufacturing sector as a whole (organised plus unorganised). Further, the share of UMS in the manufacturing sector is also found out at the state level for a number of variables like number of units, number of workers, gross value added, fixed capital and output. 1. All-India Level Scenario At the all-india level about 99 per cent of enterprises in the manufacturing sector account for 86 per cent of workers, 25 per cent of gross value added, 21 per cent of fixed capital and 17 per cent of the output in UMS. Table 3 shows that while the unorganised manufacturing sector continues to absorb a large chunk of labour force, its contribution to the total output and value added is not substantial. This may be attributed to the sector s smaller share in the capital stock implying that the production process in UMS is labour intensive. Within the UMS there is a preponderance of OAME in terms of number of enterprises and workers. In the OAME 68 per cent of the workers of the unorganised manufacturing are employed, contributing 42 per cent to value added whereas the DME with a share of 17 per cent of workers is contributing 33 per cent to value added. It, therefore, seems that OAME is absorbing the workers who are displaced from the agricultural sector. The analysis of UMS by location (rural and urban) adds strength to the above argument. The rural manufacturing has 92.7, 5.3 and 2.1 per cent of its units in OAME, NDME and DME respectively as compared to 70.9, 21.3 and 7.9 per cent respectively in the urban areas. 80 per cent of the workers engaged in

LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 411 Table 3 Percentage Share of Unorganised Manufacturing in Total Manufacturing Sector and that of Various Enterprise Types within the Unorganised Manufacturing at all-india Level for 2000-01 Sector Units Workers G.V.A. F.C. Output Unorganised manufacturing sector 99.2 86.4 25.2 20.5 16.9 to total manufacturing sector Within unorganised manufacturing (combined) OAME 86.1 67.6 42.3 36.2 27.1 NDME 10.1 15.0 25.0 30.3 20.5 DME 3.8 17.4 32.7 33.5 52.3 Within unorganised manufacturing (rural) OAME 92.7 79.8 63.1 59.2 53.0 NDME 5.3 8.1 13.8 16.8 14.5 DME 2.1 12.1 23.1 24.0 34.7 Within unorganised manufacturing (urban) OAME 70.9 45.2 25.8 25.4 14.5 NDME 21.3 27.7 33.9 36.7 23.5 DME 7.9 27.1 40.3 37.9 60.5 Note: G.V.A. = Gross Value Added; F.C. = Fixed Capital. Source: ASI (Summary Results) Factory Sector and NSS (56th Round), 2000-01. OAME contribute 63 per cent to the value added in rural area as compared to the DME that is contributing 23 per cent to the value added by employing 12 per cent of workers in the rural area. However, the differences are not so glaring in the urban area. The DME with 27 per cent of workforce contributes 40 per cent to value added whereas OAME contributes only 26 per cent to value added by employing 45 per cent of workers. An interesting observation is that while in the rural area the workforce and value addition in UMS come from OAME, in the urban area the same comes from NDME and DME combined. Further, the share of NDME in both workforce and value addition is considerably higher in urban area in comparison with rural area. Thus, the NDME in the urban area is growing rapidly in comparison with its counterpart in the rural area because of the ancillarisation of enterprises and the availability of infrastructure. 2. State Level Scenario All states except that of Goa, Gujarat, Haryana, Maharashtra, Punjab, and Tamil Nadu have more than 99 per cent of their manufacturing sector in the unorganised sector. However, there are variations among these states in terms of other variables. West Bengal and Uttar Pradesh account for 99 per cent units and employ 90 per cent of the labour force in unorganised manufacturing but their percentage share in terms of value added is only 40 to 50 per cent and that in output of about 25 to 35 per cent, implying labour intensive production approach and the dependence of these states economy on the SSI sector. On the other hand, Andhra Pradesh, Bihar, Karnataka, Madhya Pradesh, Orissa and Rajasthan also have sizeable number of units and employment in the unorganised manufacturing, but they account for a meagre share in the total manufacturing in terms of value added (16-30 per cent) and output (10-20 per cent). The state of Jammu & Kashmir shows exceptionally high share for UMS across variables and this may be attributed to its relatively less developed manufacturing sector (see Table 4).

412 THE INDIAN JOURNAL OF LABOUR ECONOMICS Table 4 Structure of Unorganised Manufacturing Sector across States (Unorganised Sector as a Ratio to Manufacturing Sector as a Whole) State Units Workers G.V.A F.C Output Andhra Pradesh 99.1 81.2 26.2 17.4 12.7 Bihar 99.8 92.9 33.9 15.0 19.2 Goa 97.9 73.9 6.9 8.9 3.8 Gujarat 97.5 72.9 15.3 8.9 8.7 Haryana 97.8 65.9 13.7 19.0 7.5 Himachal Pradesh 99.5 83.9 17.5 17.5 24.1 Jammu & Kashmir 99.8 96.3 83.0 79.0 62.9 Karnataka 99.3 85.0 22.2 15.5 13.6 Kerala 99.1 80.3 32.7 34.5 17.4 Madhya Pradesh 99.5 88.3 16.5 14.3 10.4 Maharashtra 98.5 78.4 16.8 18.7 20.4 Orissa 99.8 95.7 28.1 9.5 16.2 Punjab 97.9 72.8 29.8 40.0 6.2 Rajasthan 99.2 86.7 26.5 23.6 9.7 Tamil Nadu 98.7 78.8 23.0 24.1 13.6 Uttar Pradesh 99.6 97.7 35.0 24.3 20.3 West Bengal 99.8 92.8 50.8 28.2 37.0 All- India 99.2 86.4 25.2 20.5 16.9 Note and Source: Same as in Table 3. III. COMPOSITION OF UNORGANISED MANUFACTURING SECTOR ACROSS STATES The composition of the unorganised manufacturing sector is studied for different states, classifying the sector by different categories of enterprises (i.e. OAME, NDME and DME) and their area of operation (rural or urban) and also at the combined level for different variables. To take care of the size effect, percentage share of population (as percentage of total population in India) is also presented in the table for rural, urban and combined for different states. Looking at the composition of the UMS in terms of enterprises and the composition of population in various states at the combined level (rural plus urban) it appears that the UMS is doing well in the states of Andhra Pradesh, Jammu & Kashmir, Karnataka, Orissa, Tamil Nadu and West Bengal. This is because the percentage of enterprises set up in each of these states is more than the percentage share of population it inhabits. Further, the above six states combined together have a share of about 50 per cent of enterprises in the UMS. While Orissa and Jammu & Kashmir have most of the enterprises in the OAME sector, enterprises in Andhra Pradesh are mostly in OAME and NDME sector. Karnataka, Tamil Nadu and West Bengal are doing well in all the enterprise types. But Tamil Nadu has most of its enterprises in the DME sector and West Bengal s UMS is dominated by OAME sector. The composition at separate rural and urban classification does not change much from the scenario at combined level. Andhra Pradesh, Jammu & Kashmir, Karnataka, Kerala, Orissa, Tamil Nadu and West Bengal are the states that have a greater percentage share of enterprises in the rural UMS as compared to their share in India s rural population. Thus, these are the states that are performing well in terms of putting up enterprises more than proportionately to their population. Similarly, in the urban UMS, states showing better performance are Andhra Pradesh, Jammu & Kashmir, Karnataka, Punjab, Tamil Nadu, Uttar Pradesh and West Bengal (Table 5).

Table 5 Percentage Share of Number of Enterprises in Unorganised Manufacturing Sector and Population in Major States Rural Urban Combined State OAME NDME DME All %Pop. OAME NDME DME All %Pop. OAME NDME DME All %Pop. Andhra Pradesh 10. 10.0 10.1 10.1 7.46 8.9 5.9 4.6 7.9 7.27 9.8 7.4 6.7 9.4 7.41 Bihar 9.5 5.1 2.1 9.1 12.83 3.9 2.5 0.9 3.3 5.12 8.1 3.5 1.4 7.4 10.69 Goa 0.1 0.5 0.2 0.2 0.09 0.1 0.2 0.2 0.1 0.23 0.1 0.3 0.2 0.1 0.13 Gujarat 2.0 1.7 5.5 2.1 4.27 5.0 7.2 9.3 5.8 6.62 2.7 5.2 7.8 3.2 4.93 Haryana 0.8 1.5 0.5 0.8 2.02 1.6 2.5 1.9 1.8 2.14 1.0 2.2 1.4 1.1 2.06 Himachal Pradesh 0.7 1.0 0.7 0.8 0.74 0.1 0.2 0.1 0.1 0.20 0.6 0.5 0.3 0.6 0.59 Jammu & Kashmir 1.2 1.0 0.5 1.2 1.03 1.6 0.8 0.5 1.3 0.88 1.3 0.8 0.5 1.2 0.99 Karnataka 5.5 6.5 13.1 5.8 4.70 7.8 4.4 4.5 6.8 6.28 6.1 5.2 7.8 6.1 5.14 Kerala 3.0 11.9 7.2 3.5 3.18 1.6 2.2 1.7 1.7 2.89 2.6 5.8 3.8 3.0 3.1 Madhya Pradesh 6.3 2.5 2.9 6.0 8.22 6.1 3.7 2.8 5.3 7.04 6.2 3.2 2.8 5.8 7.9 Maharashtra 5.6 5.2 7.3 5.6 7.51 9.9 12.7 19.4 11.2 14.36 6.6 10.0 14.8 7.3 9.42 Orissa 8.1 2.7 1.8 7.7 4.21 1.4 0.9 0.6 1.3 1.93 6.5 1.6 1.1 5.8 3.58 Punjab 1.5 2.8 1.1 1.6 2.17 2.5 4.3 4.3 3.0 2.89 1.8 3.7 3.1 2.0 2.37 Rajasthan 3.4 2.2 1.6 3.3 5.83 5.3 3.0 2.4 4.6 4.62 3.8 2.7 2.1 3.7 5.49 Tamil Nadu 6.8 10.2 11.4 7.1 4.70 13.5 13.1 13.6 13.4 9.61 8.5 12.1 12.7 9.0 6.07 Uttar Pradesh 14.3 16.6 19.4 14.5 18.58 14.0 12.8 9.8 13.4 12.83 14.2 14.2 13.5 14.2 16.99 West Bengal 18.1 14.4 13.2 17.8 7.78 13.4 11.3 9.9 12.7 7.84 16.9 12.5 11.2 16.3 7.79 Source: NSSO (56th Round), 2000-01 and Census of India (2001). LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 413

414 THE INDIAN JOURNAL OF LABOUR ECONOMICS Comparing the number of enterprises in the organised manufacturing sector and population it is found that the states of Andhra Pradesh, Gujarat, Haryana, Karnataka, Maharashtra, Punjab, and Tamil Nadu account for higher share of enterprises relative to its population (Table 6). Further, if we take the unorganised manufacturing sector (small scale industries) and the organised manufacturing (large scale industries) it is seen that the three states of Andhra Pradesh, Karnataka and Tamil Nadu are progressively putting up relatively more enterprises. It brings forth the issue of how the states where organised manufacturing is well established proliferates the unorganised manufacturing sector. The inter-linkages between the two sectors of manufacturing and externalities provided by the organised manufacturing sector can work in favour of the unorganised manufacturing sector. The SSI in the states of Andhra Pradesh, Karnataka and Tamil Nadu seem to be reaping the benefits of a well-established organised manufacturing sector. 2 Table 6 Percentage Share of Some Variables in the Organised Manufacturing Sector and Population in Major States State Number of enterprises Number of workers Percentage of population Andhra Pradesh 10.7 13.1 7.41 Bihar 2.3 3.1 10.69 Goa 0.4 0.4 0.13 Gujarat 10.7 9.5 4.93 Haryana 3.4 3.7 2.06 Himachal Pradesh 0.4 0.5 0.59 Jammu & Kashmir 0.3 0.3 0.99 Karnataka 5.3 6.2 5.14 Kerala 3.7 4.5 3.1 Madhya Pradesh 3.4 4.4 7.9 Maharashtra 14.1 14.0 9.42 Orissa 1.3 1.7 3.58 Punjab 5.4 4.8 2.37 Rajasthan 3.9 3.0 5.49 Tamil Nadu 15.7 15.9 6.07 Uttar Pradesh 7.9 2.2 16.99 West Bengal 4.6 7.8 7.79 Source: ASI (Summary Results) Factory Sector, 2000-01 and Census of India (2001). The above analysis becomes more meaningful if we compare the composition of workers in the unorganised manufacturing and that of population in various states (Table 7). It is the same six states (Andhra Pradesh, Jammu & Kashmir, Karnataka, Orissa, Tamil Nadu and West Bengal) having more enterprises in proportion to their population that are found to be providing employment more than proportionately to the population of the state concerned. The percentage share of workers in UMS at combined level is more than the percentag share of population in the states of Andhra Pradesh, Jammu & Kashmir, Karnataka, Orissa, Tamil Nadu, and West Bengal, though the composition differs at enterprise level. While Andhra Pradesh, Jammu & Kashmir and Orissa are doing better in providing employment in the OAME enterprise, Karnataka has a higher share in employment in the NDME and DME enterprise type. It is only in the states of Tamil Nadu and West Bengal that the people are getting absorbed in all the enterprise types of the unorganised manufacturing sector. The composition of workers in rural-urban classification throws more light on this issue. Employment is found to be more than proportionate to its labour force in the rural unorganised

Table 7 Percentage Share of Number of Workers in the Unorganised Manufacturing Sector and Population in Major States Rural Urban Combined State OAME NDME DME All %Pop. OAME NDME DME All %Pop. OAME NDME DME All %Pop. Andhra Pradesh 10.2 10.2 8.2 9.9 7.46 9.3 5.7 4.5 7.0 7.27 10.0 7.2 6.2 8.9 7.41 Bihar 9.9 4.9 2.5 8.6 12.83 4.1 2.3 0.7 2.7 5.12 8.5 3.2 1.5 6.5 10.69 Goa 0.2 0.5 0.1 0.2 0.09 0.1 0.2 0.2 0.1 0.23 0.1 0.3 0.1 0.2 0.13 Gujarat 1.9 1.7 5.9 2.3 4.27 4.9 7.7 10.0 7.1 6.62 2.6 5.6 8.2 4.0 4.93 Haryana 0.7 1.4 0.8 0.7 2.02 1.5 2.3 1.9 1.8 2.14 0.9 2.0 1.4 1.1 2.06 Himachal Pradesh 0.5 0.9 0.6 0.6 0.74 0.1 0.2 0.1 0.1 0.20 0.4 0.4 0.3 0.4 0.59 Jammu & Kashmir 1.4 0.8 0.6 1.3 1.03 2.1 0.7 0.4 1.3 0.88 1.6 0.8 0.5 1.3 0.99 Karnataka 4.5 6.8 9.9 5.3 4.70 7.3 4.4 4.7 5.8 6.28 5.1 5.2 7.1 5.5 5.14 Kerala 2.3 12.2 6.2 3.5 3.18 1.4 2.1 1.8 1.7 2.89 2.0 5.6 3.8 2.9 3.1 Madhya Pradesh 6.2 2.4 3.2 5.5 8.22 6.5 3.7 2.5 4.6 7.04 6.3 3.3 2.8 5.2 7.9 Maharashtra 4.9 5.3 7.1 5.2 7.51 9.9 13.0 18.9 13.2 14.36 6.1 10.3 13.6 8.0 9.42 Orissa 10.3 2.4 1.4 8.6 4.21 1.4 0.9 0.6 1.0 1.93 8.2 1.4 1.0 5.9 3.58 Punjab 1.2 2.5 2.1 1.4 2.17 2.2 4.1 3.8 3.1 2.89 1.4 3.6 3.0 2.0 2.37 Rajasthan 3.0 2.4 1.4 2.7 5.83 5.2 2.9 2.3 3.8 4.62 3.5 2.7 1.9 3.1 5.49 Tamil Nadu 6.1 10.9 10.1 7.0 4.70 12.7 13.5 15.0 13.5 9.61 7.7 12.6 12.8 9.3 6.07 Uttar Pradesh 14.5 16.2 26.1 16.0 18.58 16.6 12.5 9.6 13.5 12.83 15.0 13.8 17.1 15.2 16.99 West Bengal 19.7 14.8 12.5 18.4 7.78 11.8 11.0 10.1 11.1 7.84 17.8 12.3 11.1 15.8 7.79 Source: NSSO (56th Round), 2000-01 and Census of India (2001). LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 415

416 THE INDIAN JOURNAL OF LABOUR ECONOMICS manufacturing sector in the states of Andhra Pradesh, Jammu & Kashmir, Karnataka, Kerala, Orissa, Tamil Nadu and West Bengal. In the urban area, Gujarat, Jammu & Kashmir, Punjab, Tamil Nadu, and West Bengal are doing particularly well in terms of providing employment. Thus, it could be deduced that employment in SSI in Andhra Pradesh, Jammu & Kashmir and Orissa is relatively more in rural areas and in the OAME enterprise type. In Karnataka people are employed in rural areas but they are working in NDME and DME enterprise types. Tamil Nadu and West Bengal are seen to be absorbing labour in both rural and urban areas and in every enterprise type. But there is more concentration of employment in OAME enterprises and in rural area in West Bengal whereas the concentration of employment in Tamil Nadu is towards urban area and in the DME sector. Further, in the organised manufacturing sector it is found that Andhra Pradesh, Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Punjab, and Tamil Nadu are employing more than proportionately to their share in population. A comparison of these states with the states doing well in terms of employment in the unorganised manufacturing sector brings out that the states of Andhra Pradesh, Karnataka and Tamil Nadu are performing very well in both the sectors of manufacturing. 3 The policy option in such a situation would be to encourage the unemployed, especially the skilled youth, to establish small scale units and provide employment opportunities to the people in the states of Gujarat, Haryana, Maharashtra, and Punjab. In these states the organised sector is well established and also employs a large number of workers. These states have a potential to generate employment in the unorganised manufacturing sector through subcontracting of product lines, ancillarisation of industries and by the transfer of technology. Finally, the structure, development and concentration (rural/urban) of UMS seem to be dependent on the overall economic development of the state as a whole and the industrial sector in particular. The UMS in the states of Andhra Pradesh, Bihar, Himachal Pradesh, Jammu & Kashmir, Kerala, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh, and West Bengal are rural in character and concentrated in the OAME enterprises. It shows a situation of distressed occupation; that is, after getting relieved from agriculture these people have started their own business for the sake of being employed. This sector of UMS is particularly large and growing in these poor states and needs immediate attention of the policy makers. On the other hand, the UMS in the states of Gujarat, Karnataka, Maharashtra, Punjab and Tamil Nadu are mostly concentrated either in the NDME or in DME or in both the sectors because of the availability of infrastructure, banking, and other facilities of a well developed township. Although it is true that to improve the existing situation there is a need for long-term planning and concrete measures, some short-term measures could be taken to enhance the labour productivity, which, in turn, can raise their living standard and work efficiency. Social security measures, imparting skills to take up these occupations willingly and not as a distressed occupation and finally, assured, timely and sufficient loans to individuals or to groups are some of the measures which once initiated will show their impact on productivity level. IV. LABOUR PRODUCTIVITY, CAPITAL PRDUCTIVITY, CAPITAL INTENSITY IN THE ORGANISED AND UNORGANISED MANUFACTURING SECTORS It can be seen from Tables 8 and 9 that labour productivity in UMS is far below the OMS. Labour productivity is measured as gross value added per worker. Capital productivity is measured as the ratio of gross value added to fixed capital and capital intensity as the ratio of capital to labour. Labour productivity in organised manufacturing is Rs 3.05 lakh at the all India

LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 417 level whereas in unorganised manufacturing it is only Rs. 0.16 lakh which is a little less than one-twentieth of the OMS. Labour productivity in OMS varies from Rs. 1 lakh to Rs. 10 lakh as compared to UMS where it varies from Rs. 5,000 to Rs. 55,000. Table 8 Labour Productivity, Capital-Labour Ratio and Capital Productivity in the Organised Manufacturing Sector a Labour productivity Capital intensity Capital (in Rs.) (in Rs.) productivity State O/L K/L O/K Andhra Pradesh 145019 344808 0.42 (16) (14) (11) Bihar 324013 952064 0.34 (9) (6) (15) Goa 812816 1308205 0.62 (2) (2) (1) Gujarat 415241 1301930 0.32 (5) (3) (16) Haryana 326650 642677 0.51 (8) (11) (7) Himachal Pradesh 519481 1143350 0.45 (3) (5) (10) Jammu & Kashmir 108775 184312 0.59 (17) (17) (4) Karnataka 284298 733037 0.39 (11) (10) (13) Kerala 159008 259596 0.61 (14) (16) (3) Madhya Pradesh 406238 804790 0.5 (6) (8) (8) Maharashtra 461109 826271 0.56 (4) (7) (5) Orissa 310655 1157039 0.27 (10) (4) (17) Punjab 188940 305175 0.62 (13) (15) (1) Rajasthan 368212 778795 0.47 (7) (9) (9) Tamil Nadu 219066 404482 0.54 (12) (12) (6) Uttar Pradesh 1036213 2811710 0.37 (1) (1) (14) West Bengal 156089 378512 0.41 (15) (13) (12) All-India 305646 684818 0.45 Note: a. Figures in the parentheses represent rank of the state. Source: ASI (Summary Results) Factory Sector, 2000-01. From Table 9 it can be seen that Uttar Pradesh, Goa, Himachal Pradesh, Maharastra and Gujarat are the top five states in terms of labour productivity. An apparent reason for these states having higher labour productivity is that UMS in these states are highly capital intensive. Further, the states of Jammu and Kashmir, Andhra Pradesh and West Bengal, which show very

418 THE INDIAN JOURNAL OF LABOUR ECONOMICS low labour productivity, are the less capital intensive (or labour intensive) states. Thus, the more capital intensive UMS of a state is, the more productive its labour becomes. In the unorganised manufacturing sector, Table 9 shows that Punjab, Gujarat, Haryana, and Maharastra have the highest labour productivity of more than Rs. 25,000 followed by the states like Himachal Pradesh, Goa, Jammu & Kashmir and Rajasthan having labour productivity in the range of Rs. 20,000 to 25,000. Kerala and Tamil Nadu also have higher labour productivity as compared to the all India average. The remaining states have lower productivity with Orissa being the lowest at only Rs. 5459 which is even less than one fifth of Punjab. At enterprise level, Haryana, Jammu & Kashmir, Punjab, Gujarat and Rajasthan are the states, which have higher labour productivity in OAME enterprise type. But states of Himachal Pradesh, Gujarat, Punjab, Maharastra and Jammu & Kashmir have higher labour productivity in the NDME enterprise. In case of DME sector Himachal Pradesh, Goa, Punjab, Maharashtra, and Jammu & Kashmir have higher labour productivity. Orissa is having the lowest labour productivity in all types of enterprises. If we look at the rural-urban classification in labour productivity it is seen that labour productivity is higher in urban areas as compared to rural areas in all the three types of enterprises across the states, barring a few exceptions. In rural areas, Punjab, Jammu & Kashmir and Haryana have the highest labour productivity, whereas in the urban area it is the states of Himachal Pradesh, Punjab and Gujarat that are at the top. However, labour productivity is lowest in the states of Andhra Pradesh and Madhya Pradesh with Orissa at the bottom in both rural and urban area. Capital productivity in the unorganised industries as a whole is higher (0.58) than that of organised industries (0.45). It is because in UMS the stock of fixed capital is far smaller in comparison with that of OMS. The states showing capital productivity of nearly one and over are Bihar, Orissa, and West Bengal and these are also the states with lower labour productivity. It becomes clearer if we look at the relative ranking of these states. While West Bengal, Orissa and Bihar rank first, second and third in terms of capital productivity, their rank in terms of labour productivity is fourteenth, seventeenth and thirteenth respectively out of the seventeen states considered under this study. Further, the states of Haryana, Punjab, and Himachal Pradesh which rank seventeenth, sixteenth and fifteenth in terms of capital productivity correspondingly rank third, first and fifth in terms of labour productivity. In comparison with rural areas, urban areas have lower capital productivity across states and in all enterprises (Table 10). In the OMS the capital intensity is Rs. 6.84 lakh per worker and in UMS it is only Rs. 27,000 per worker. The variation in capital intensity in OMS is from Rs. 1 lakh to Rs. 28 lakh and in the UMS it varies between Rs. 5,000 and Rs. 1 lakh (Tables 10 and 11). Haryana, Punjab, Maharashtra, Gujarat and Himachal Pradesh are highly capital intensive in the UMS. But Orissa, West Bengal, Bihar, Andhra Pradesh, Madhya Pradesh and Uttar Pradesh show labour intensiveness in their production process as shown from their ranking with Orissa ranking last among all the states. Further, the urban area, as expected, is more capital intensive than the rural area in all enterprises across states. For each state, relative labour and capital productivities are obtained by dividing labour and capital productivities in unorganised manufacturing units by those in the organised manufacturing units. It is seen from Table 12, that the relative labour productivity is less than unity in all states. It implies that the labour productivity in the unorganised manufacturing is less than that in organised manufacturing. On the other hand, relative capital productivity is greater than unity, that is capital productivity in unorganised manufacturing is higher as compared to organised manufacturing in ten states. Further, relative capital productivity exceeds relative

Table 9 Labour Productivity (in Rs.) by Enterprise Type in Major States in the UMS a Rural Urban Combined State OAME NDME DME All OAME NDME DME All OAME NDME DME All Andhra Pradesh 7940 14347 16446 9324 13468 24657 28680 18637 9157 19611 21337 11906 (13) (16) (15) (15) (11) (14) (14) (15) (12) (16) (14) (15) Bihar 10335 19743 30628 11472 15770 27188 27884 19429 10945 23284 29906 12629 (9) (10) (6) (11) (8) (11) (16) (13) (9) (12) (10) (13) Goa 7358 32146 49113 16634 13444 34074 51700 32426 8373 32847 50641 21082 (15) (2) (2) (7) (12) (8) (2) (6) (15) (7) (2) (6) Gujarat 13111 27737 23029 16987 22026 40634 40126 34643 17086 39298 34559 27967 (5) (5) (10) (6) (4) (3) (7) (3) (4) (2) (8) (2) Haryana 16196 26330 24490 18814 22550 33781 45034 32742 18834 32000 39638 26838 (2) (6) (9) (3) (3) (9) (4) (5) (1) (8) (4) (3) Himachal Pradesh 10501 38301 49814 18797 28362 42075 56460 40389 11523 39415 51079 21234 (7) (1) (1) (4) (1) (1) (1) (1) (7) (1) (1) (5) Jammu & Kashmir 18357 31290 31513 19820 17498 34746 40330 22305 18084 33444 35541 20701 (1) (3) (5) (2) (5) (7) (6) (10) (2) (5) (7) (7) Karnataka 8734 19366 11247 10403 13574 27833 33997 21055 10362 23983 19582 14375 (10) (11) (16) (12) (10) (10) (11) (11) (10) (11) (16) (11) Kerala 10474 20166 26061 16484 14078 39125 33456 28422 11041 24857 28009 18966 (8) (9) (8) (8) (9) (4) (12) (7) (8) (9) (12) (9) Madhya Pradesh 7049 18171 9249 7585 10402 24005 37047 17333 7864 22531 22803 10635 (16) (12) (17) (16) (16) (16) (9) (16) (16) (13) (13) (16) Maharashtra 12479 24510 22552 15128 16648 37436 44278 33025 14090 35142 39196 25552 (6) (8) (11) (9) (7) (5) (5) (4) (6) (4) (5) (4) Orissa 4342 14247 16958 4814 9781 24090 23538 15138 4567 18227 19182 5459 (17) (17) (14) (17) (17) (15) (17) (17) (17) (17) (17) (17) Punjab 14814 25933 37504 20503 23668 40715 47378 37538 18043 37100 44325 29928 (4) (7) (3) (1) (2) (2) (3) (2) (3) (3) (3) (1) Rajasthan 15229 29585 31690 17249 16875 34755 40074 24504 15806 33169 37319 20366 (3) (4) (4) (5) (6) (6) (8) (8) (5) (6) (6) (8) Tamil Nadu 8495 17473 21925 11979 11529 27001 34768 22790 9681 24127 30218 17542 (11) (13) (12) (10) (15) (12) (10) (9) (11) (10) (9) (10) Uttar Pradesh 7412 16060 17922 10187 13014 24794 28411 18988 8873 21221 21182 12964 (14) (15) (13) (14) (13) (13) (15) (14) (13) (14) (15) (12) West Bengal 8210 16249 26299 10216 11837 23470 30126 19539 8775 20460 28198 12523 (12) (14) (7) (13) (14) (17) (13) (12) (14) (15) (11) (14) All-India 8783 19103 21210 11120 14595 31328 38064 25598 10154 27079 30481 16233 Note: a. Figures in the parentheses represent rank of the state (i.e. Rank 1 represents state with highest labour productivity). Source: NSSO (56th Round), 2000-01. LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 419

Table 10 Capital Productivity by Enterprise Type in Major States b Rural Urban Combined State OAME NDME DME All OAME NDME DME All OAME NDME DME All Andhra Pradesh 0.85 0.59 0.87 0.81 0.63 0.56 0.62 0.61 0.77 0.57 0.72 0.71 (5) (9) (8) (7) (5) (4) (5) (3) (5) (5) (4) (5) Bihar 1.01 1.09 1.74 1.06 0.83 0.79 0.72 0.8 0.98 0.9 1.29 0.99 (4) (2) (1) (3) (1) (1) (2) (1) (4) (1) (1) (3) Goa 0.39 0.56 0.46 0.47 0.39 0.34 0.65 0.65 0.39 0.45 0.56 0.47 (17) (12) (14) (15) (13) (16) (3) (12) (16) (12) (9) (14) Gujarat 0.79 0.61 1.11 0.87 0.44 0.52 0.64 0.54 0.54 0.52 0.7 0.59 (7) (7) (5) (5) (10) (6) (4) (7) (11) (8) (5) (9) Haryana 0.44 0.38 0.46 0.43 0.28 0.28 0.36 0.32 0.35 0.32 0.37 0.34 (16) (17) (14) (17) (17) (17) (16) (17) (17) (17) (16) (17) Himachal Pradesh 0.48 0.51 0.43 0.47 0.34 0.42 0.46 0.41 0.45 0.48 0.44 0.45 (15) (14) (16) (15) (15) (11) (13) (15) (15) (10) (15) (15) Jammu & Kashmir 1.17 0.59 0.73 1.03 0.73 0.38 0.43 0.54 0.99 0.43 0.54 0.77 (3) (9) (10) (4) (4) (14) (14) (7) (3) (14) (10) (4) Karnataka 0.74 1.07 0.96 0.84 0.59 0.48 0.43 0.49 0.67 0.6 0.53 0.6 (8) (3) (7) (6) (6) (9) (14) (9) (6) (3) (12) (7) Kerala 0.6 0.55 0.54 0.56 0.52 0.62 0.53 0.57 0.58 0.58 0.54 0.56 (14) (13) (12) (13) (8) (3) (9) (5) (9) (4) (10) (10) Madhya Pradesh 0.81 0.66 1.01 0.81 0.46 0.44 0.55 0.48 0.65 0.47 0.61 0.6 (6) (5) (6) (7) (9) (10) (8) (10) (7) (11) (8) (7) Maharashtra 0.73 0.65 0.52 0.65 0.39 0.4 0.52 0.45 0.52 0.42 0.52 0.49 (9) (6) (13) (11) (13) (13) (10) (14) (13) (15) (13) (13) Orissa 1.29 0.5 1.17 1.16 0.79 0.49 0.56 0.6 1.23 0.49 0.81 1 (2) (15) (4) (2) (3) (7) (6) (4) (2) (9) (3) (2) Punjab 0.69 0.48 0.4 0.52 0.34 0.35 0.36 0.35 0.46 0.37 0.37 0.39 (11) (16) (17) (14) (15) (15) (16) (16) (14) (16) (16) (16) Rajasthan 0.65 0.73 1.18 0.69 0.42 0.49 0.52 0.47 0.54 0.54 0.62 0.55 (13) (4) (3) (9) (12) (7) (10) (11) (11) (7) (7) (11) Tamil Nadu 0.7 0.57 0.61 0.64 0.44 0.42 0.5 0.46 0.55 0.45 0.52 0.51 (10) (11) (11) (12) (10) (11) (12) (12) (10) (12) (13) (12) Uttar Pradesh 0.67 0.6 0.76 0.68 0.57 0.53 0.56 0.55 0.63 0.55 0.66 0.62 (12) (8) (9) (10) (7) (5) (6) (6) (8) (6) (6) (6) West Bengal 1.46 1.13 1.54 1.43 0.8 0.77 0.78 0.78 1.24 0.86 1.02 1.08 (1) (1) (2) (1) (2) (2) (1) (2) (1) (2) (2) (1) All-India 0.86 0.67 0.78 0.81 0.49 0.44 0.51 0.48 0.68 0.48 0.57 0.58 Note: b. figures in the parentheses represent rank of the state (i.e. Rank 1 represents state with highest capital productivity). Source: NSSO (56th Round), 2000-01. 420 THE INDIAN JOURNAL OF LABOUR ECONOMICS

Table 11 Capital-Labour Ratio (in Rs.) by Enterprise Type in Seventeen Major States c Rural Urban Combined State OAME NDME DME All OAME NDME DME All OAME NDME DME All Andhra Pradesh 9308 24321 19006 11523 21223 44020 46015 30671 11930 34378 29804 16831 (14) (14) (12) (13) (14) (15) (14) (14) (14) (15) (14) (14) Bihar 10189 18068 17630 10807 19023 34211 38800 24160 11181 25746 23205 12748 (13) (16) (13) (14) (15) (16) (16) (17) (15) (16) (17) (15) Goa 18956 57766 35429 35429 34699 99696 79069 70451 21583 73013 90524 45295 (5) (3) (2) (4) (7) (3) (7) (5) (7) (7) (4) (6) Gujarat 16564 45345 20687 19484 50585 78295 63061 63799 31734 74882 49262 47073 (8) (6) (11) (8) (4) (7) (11) (6) (3) (6) (10) (4) Haryana 36668 68499 53466 43631 79546 111480 125832 103561 54469 101206 106826 78155 (1) (2) (4) (1) (2) (2) (2) (2) (1) (1) (3) (1) Himachal Pradesh 22064 75612 115116 40149 84229 99666 121601 99058 25620 82713 116350 46798 (3) (1) (1) (2) (1) (4) (3) (3) (6) (4) (2) (5) Jammu & Kashmir 15659 52962 43366 19267 24001 92237 93402 41101 18306 77443 66223 27009 (9) (5) (7) (9) (10) (6) (4) (11) (9) (5) (6) (10) Karnataka 11780 18090 11743 12424 23025 58140 79775 42873 15561 39931 36671 23777 (11) (15) (16) (12) (11) (11) (6) (10) (11) (12) (12) (11) Kerala 17590 36527 47986 29320 26944 63545 62799 50087 19060 43212 51888 33637 (6) (9) (5) (5) (8) (10) (12) (8) (8) (11) (9) (9) Madhya Pradesh 8722 27595 9173 9403 22642 55065 66859 36344 12101 48126 37299 17832 (15) (12) (17) (15) (13) (12) (10) (12) (13) (10) (11) (13) Maharashtra 17052 37910 43475 23126 42720 93630 84803 72903 26970 83742 75138 52118 (7) (8) (6) (7) (5) (5) (5) (4) (5) (3) (5) (3) Orissa 3354 28776 14450 4144 12358 48860 41739 25162 3726 36898 23673 5458 (17) (11) (15) (17) (17) (13) (15) (15) (17) (14) (16) (17) Punjab 21513 54348 93338 39177 70138 114944 130098 105832 39245 100125 118734 76056 (4) (4) (3) (3) (3) (1) (1) (1) (2) (2) (1) (2) Rajasthan 23518 40670 26877 24930 39843 70985 77058 52600 29243 61681 60567 36817 (2) (7) (9) (6) (6) (8) (8) (7) (4) (8) (7) (7) Tamil Nadu 12170 30427 35869 18623 26012 63720 69818 49600 17582 53677 57790 34563 (10) (10) (8) (10) (9) (9) (9) (9) (10) (9) (8) (8) Uttar Pradesh 11127 26749 23689 14874 22729 46827 50430 34217 14152 38612 32001 20976 (12) (13) (10) (11) (12) (14) (13) (13) (12) (13) (13) (12) West Bengal 5628 14405 17102 7138 14856 30572 38410 24976 7065 23833 27675 11552 (16) (17) (14) (16) (16) (17) (17) (16) (16) (17) (15) (16) All-India 10184 28706 27244 13743 30007 70755 74756 53437 14862 56142 53379 27761 Note: c. Figures in the parentheses represent rank of the state (i.e. Rank 1 represents state with highest capital-labour ratio). Source: NSSO (56th Round), 2000-01. LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 421

422 THE INDIAN JOURNAL OF LABOUR ECONOMICS labour productivity, in almost all cases. The states have been classified into three sub-groups of low, medium and high relative labour productivity (Table 13). The states of Bihar, Goa, Himachal Pradesh, Madhya Pradesh, Orissa, and Uttar Pradesh fall in low relative labour productivity, that is labour productivity in the unorganised sector is far less than that of labour productivity in organised sector. On the other hand, states of Punjab, Kerala, and Jammu & Kashmir fall under the category of high relative labour productivity; that is, the labour productivity in the unorganised sector is lower than the organised sector, but the gap is relatively small. Table 12 Relative Labour and Capital Productivities (Ratio of Productivities in Unorganised and Organised Manufacturing) State Relative labour productivity Relative capital productivity Andhra Pradesh 0.082 1.682 Bihar 0.039 2.911 Goa 0.026 0.749 Gujarat 0.067 1.863 Haryana 0.082 0.676 Himachal Pradesh 0.041 0.999 Jammu & Kashmir 0.190 1.299 Karnataka 0.051 1.559 Kerala 0.119 0.921 Madhya Pradesh 0.026 1.182 Maharashtra 0.055 0.879 Orissa 0.018 3.725 Punjab 0.158 0.636 Rajasthan 0.055 1.170 Tamil Nadu 0.080 0.937 Uttar Pradesh 0.013 1.677 West Bengal 0.080 2.629 Source: NSSO (56th Round) and ASI (SR) Factory Sector, 2000-01. Table 13 Classification of States Based on Their Relative Productivities Relative labour productivity range States 0.01 0.05 Bihar, Goa, Himachal Pradesh, Madhya Pradesh, (Low) Orissa and Uttar Pradeh. 0.05 0.10 Andhra Pradesh, Gujarat, Haryana, Karnataka, (Medium) Maharastra, Rajasthan, Tamil Nadu, West Bengal, Union Territories and North Eastern States. 0.10 and above Jammu & Kashmir, Kerala and Punjab. (High) Source: Same as for Table 12. From the foregoing analysis it is clear that unorganised manufacturing industries, in comparison with organised manufacturing industries, generally have low labour productivity, high capital productivity and low capital intensity. These findings are in consonance with the findings of Goldar (1988). In the SSI sector, the states with higher capital intensity are found to have lower capital productivity but higher labour productivity. Whereas states that are labour intensive in their production process in the UMS sector have higher capital productivity and

LABOUR PRODUCTIVITY IN SMALL SCALE INDUSTRIES IN INDIA 423 lower labour productivity. It may be said, therefore, that labour productivity varies directly with capital intensity and inversely with capital productivity. Although these are only partial productivities, which cannot give the entire productivity scenario, yet they act as important indicators. V. EXPLORING SOME INTER-LINKAGES It is apparent from the above discussion that the states where organised manufacturing is well established, the unorganised manufacturing proliferates in every respect. It shows that the externalities of an established organised sector are working in favour of UMS in these states. Also, the states where the UMS are heavily concentrated provide them the extra advantage of working in cluster and as a result they do well in terms of value addition. To lend strength to this argument rank correlation between organised and unorganised manufacturing sector (in terms of number of enterprises) has been calculated to establish inter-linkages between organised and unorganised manufacturing sector. It has been found that there exists a positive and significant correlation of 0.6 between the two sectors (See Table 14). Rank correlation between the organised and urban unorganised manufacturing shows a similar trend as rho is equal to 0.87. Hence, the presence of strong inter-linkages between the two sectors is evident. On the other hand, the inter-linkages do not exist between rural unorganised and organised manufacturing sector as rank correlation between them is weak at 0.37, which is insignificant. However, there is a positive and significant correlation between rural and urban unorganised manufacturing. From the above findings it follows that the rural unorganised industries are lacking in terms of credit facility, infrastructural advantage, and technological know-how, and therefore, they are not able to reap the benefits of externalities from the organised sector. There are glaring differences in the scale and mode of production between organised and rural unorganised industries. To bridge this yawning infrastructure gap, extending some urban amenities in the rural areas seems indispensable. Table 14 Rank Correlation between Organised and Unorganised Manufacturing (In Terms of Number of Enterprises) A Search for Inter-linkages Rank correlation between Correlation coefficient (RHO) Organised and unorganised manufacturing 0.600** Organised and rural unorganised manufacturing 0.370 Organised and urban unorganised manufacturing 0.867** Rural unorganised and urban unorganised 0.619** Note: **Correlation is significant at the 0.01 level (2-tailed). Source: NSSO (56th Round) and ASI (SR) Factory Sector, 2000-01. Mitra (1998) using state level data did not find any distinct positive association between informal sector employment and incidence of poverty. However, from the discussion so far, it emerges that relatively poorer states show low levels of labour productivity whereas economically better off states are doing well in terms of productivity. To verify these findings Rank correlation between labour productivity and poverty ratios at state level has been worked out (see Table 14). It is found that a negative and significant correlation of 0.56 exists between labour productivity and poverty which means higher labour productivity does lead to poverty reduction. To pull these people out from below the poverty line, productivity in these enterprises must

424 THE INDIAN JOURNAL OF LABOUR ECONOMICS increase. Further, a negative and significant correlation exists between labour productivity and rural as well as urban poverty. Therefore, besides upgradation of technology, policy measures pertaining to job security, minimum wages, etc. applicable in organised sector can have a positive bearing on labour productivity if applied to the unorganised manufacturing sector, which in turn could be instrumental in poverty reduction. Table 14 Rank Correlation between Labour Productivity and Poverty Rank correlation between Correlation coefficient (RHO) Labour productivity and poverty (Combined) -0.560* Labour productivity and poverty (Rural) -0.562* Labour productivity and poverty (Urban) -0.699** Note: *Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2- tailed). Source: NSSO (56th Round), 2000-01 and GoI (2003-04). VI. CORRELATES OF LABOUR PRODUCTIVITY IN THE UNORGANISED MANUFACTURING SECTOR Correlation exercise has been performed to ascertain the factors that tend to have an impact on labour productivity favourably or unfavaourably. The results of the correlation exercise are presented in Table 15. Labour productivity is found to be positively and significantly correlated with the size of the enterprise defined as gross value added per enterprise and fixed capital per enterprise. It means that as size increases, labour productivity goes up and capital-intensive production process enhances efficiency of the labour force. Although outstanding loans and percentage of enterprises working under contract are not significantly related yet they show a positive relationship with labour productivity. The correlation between employment per enterprise and labour productivity is very low and insignificant, thereby, indicating the presence of disguised unemployment in the unorganised manufacturing sector. However, meaningful inference can be drawn only after looking at the relationship between the two segments separately; that is for the rural and urban areas. Further, fixed capital per enterprise has positive and significant correlation with gross value added per enterprise. The correlation exercise has also been performed for rural and urban areas separately (Table 15, Panel b and c). In the rural area correlation coefficient between labour productivity and size of enterprise is positive and significant, showing a direct relationship between size and labour productivity. On the other hand, employment per enterprise is negatively correlated with labour productivity at 5 per cent level of significance. It implies that in the rural areas the unorganised manufacturing sector is employing workers whose marginal productivity is insignificant. As argued earlier, it is because a lot of workers are absorbed in OAME enterprise in rural areas as an alternative occupation to agriculture for the sake of employment. Further, it is seen that labour productivity is positively and significantly correlated with fixed capital per enterprise. Thus, it can be inferred that there is a need for increasing investment per enterprise, which in turn can lead to increase in labour productivity. In the urban area, labour productivity is positively and significantly correlated with size of enterprise, outstanding loans per enterprise (i.e. availability of banking facilities), employment per enterprise and fixed capital per enterprise. It is an interesting situation where if we increase the scale of operation by increasing both employment and fixed capital per enterprise there is