STATUS AND POTENTIALS OF VILLAGE AGRO-PROCESSING UNITS / INDUSTRIES

Occasional Paper - 37

STATUS AND POTENTIALS OF VILLAGE AGRO-PROCESSING UNITS / INDUSTRIES

SANDIP SARKAR ANUP K. KARAN

Department of Econonnic Analysis and Research Tnsgkr ^ sfrf jjicrilui RicbixH tcp

National Bank for Agriculture and Rural Development

Mumbai

2005

Occasional Paper - 37

STATUS AND POTENTIALS OF VILLAGE AGRO-PROCESSING UNITS / INDUSTRIES

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Department of Economic Analysis and Research

Notional Bank for Agriculture and Rural Development

Mumboi

2005

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11

TABLE OF CONTENTS

Page No. 3119117 ACKNOWLEDGEMENT

EXECUTIVE SUMMARY vii

CHAPTER I INTRODUCTION 1-8

CHAPTER II RELATIVE POSITION OF VILLAGE LEVEL 9-31 AGRO-INDUSTRIES

CHAPTER i n FORWARD AND BACKWARD LINKAGE OF 33-48 AGRO-INDUSTRIES

CHAPTER IV FINANCIAL STATUS AND ACCESS TO 49-82 CREDIT MARKET

CHAPTER V CONSTRAINTS AND VIABILITY OF AGRO-INDUSTRY 83-99 AND GOVERNMENT PROGRAMMES ON RURAL INDUSTRIALISATION

CHAPTER VI SUMMARY, CONCLUSIONS AND BROAD 101-109 POLICY SUGGESTIONS

REFERENCES 111-112

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ACKNOWLEDGEMENT

This study on 'Status and Potential of Village Level Agro-Processing Units/Industries' was conducted during 2003-2004 as a part of series of occasional papers commissioned by National Bank for Agriculture and Rural Development (NABARD). In the process of completing this study we received encouragement as well as help from numerous sources. First of all our special thanks go to Department of Economic Analysis and Research of NABARD, Mumbai not only for commissioning this study but also for helping in sharpening the objectives of this paper and giving important suggestions on the first draft of this report. We are also thankful to Professor Alakh N. Sharma, Director of Institute for Human Development, for providing infrastructural and academic support at the institute.

We express our thanks to Mr. Balwant Singh Mehta and T. Shobha for data processing and generation of initial tables for this study. We are also grateful to Mr. A.J.C. Bose for editing the entire manuscript.

The views expressed in this paper are those of the authors' alone and not of the institution.

AUTHORS

ABBREVIATION

DE

DME

GVA

NDE

NDME

NDP

NIC

NSIC

NSSO

OAE

OAME

UPSS

Directory Establishments

Directory Manufacturing Establishments

Gross Value Added

Non-Directoiy Establishments

Non-Directoiy Manufacturing Establishments

Net Domestic Product

National Industrial Classification

National Small Industrial Corporation

National Sample Survey Organisation

Own Account Enterprises

Own Account Manufacturing Enterprises

Usual Principal and Subsidiary Status

vi

EXECUTIVE SUMMARY

The present study examines broadly the s ta tus and potential of village level agro-industries. It is based largely on secondary data of unorganised manufacturing enterprises survey of 2000-01. The broad objectives of this study are to find relative position of village level agro-industries in rural non-farm sector, strength of interrelationship with agricultural sector, various physical and financial constraints faced by these units and suggest policy measures to improve viability of these units and improve their market share.

Major Findings

1. Manufacturing sector occupies an important position in rural non-farm sector. Its share in rural non-farm employment is 30 per cent. However, manufacturing sector is more important in the rural informal non-farm sector employing 45 per cent of its workforce. Still, as manufacturing sector is relatively more labour-intensive than other non-farm sectors, the value added per worker is lower as compared to the other informal non-farm sectors.

2. Agro-industry dominates village level rural industry. In the year 2000-01, the share of agro-industry in village level rural industry in terms of number of enterprises, total employment and gross value added were 8 3 , 78 a n d 72 per c e n t respectively. The growth of village level agro-industry in terms of n u m b e r of un i t s , employment and value added went downhill from 1984-85 to 1989-90 and further to 1994-95. Only in 2000-01, it showed improvement or stability in these characteristics. But the productivity gap between the agro and non-agro industries is going up over the years even in the post liberalisation period. The major reason of this increased disparity lies in the substantially higher use of technology in the non-agro as compared with agro industries.

3. Among the eight industry group within agro-industry at two digit level, two major industry groups are food products and wood p r o d u c t s . Food p r o d u c t s domina t e in t e r m s of employment and value added characteristics but by dint of overwhelming presence in own account enterprises (OAME), wood p r o d u c t d o m i n a t e s in n u m b e r of e n t e r p r i s e s characteristic. In terms of size distribution, the domination of the smallest size group OAME is overwhelming. The share of

vli

OAME in the agro-industry is 90, 80. 75 and 70 per cent in terms of number of units, employment, gross value added and fixed assets characteristics respectively.

4. The direct backward production linkage of village level agro-industry is quite high at 0.5813. At the two digit level, out of nine agro-industries, only in three namely food products & beverages, wool textiles and leather & leather products, the backward production linkage is higher that that of the agro-indust ry level. Classifying agro-industries into input use pa t tern we find that only three agro-industries, viz., food products, beverages & tobacco products and wood & wood p r o d u c t s are p r imary p rocess ing and the r e s t six are secondary ( that largely processes agro- indus t ry inputs) processing. The backward linkages of the secondary processing agro-industries are much higher than that of the primary processing except for food products.

5. At the all-India level, the grouping of agro-industry in primary and secondary processing is almost similar except that cotton textiles and jute textiles are largely primary processing at all-India level. For these two agro-industries primary processing is under taken outside village level. However, in most of the primary and secondary processing agro-industries, the share of agricultural input and agro-industry inputs respectively in agro-industry output are higher at the village level than that of the all-India level. It shows that compared to the all-India level, village level agro-industries are either at the beginning of the primary processing or at the fag end of the secondary processing.

6. The forward production linkages of agro-industries are higher a t the all-India level. But even then , most of the agro-industries' output are consumed within the particular agro-industry itself. However, for three agro-industries namely jute textiles, paper products wood products around one-fifth of their output gets used in the non-agro industries largely in the packaging of the branded products. This outlet is hardly available for village level agro-industries.

7. Across size of enterprises, the GVA (gross value added) per worker is lower in agro enterprises than in the non-agro e n t e r p r i s e s . The reason is lower levels of infusion of technology in agro enterprises reflected by lower value of fixed

vi i i

asset per enterprise and per worker in the agro enterprises. Further, not only fixed asset per enterprise is lower in agro enterprises but also the capital-output ratio is higher in agro enterprises as compared to non-agro enterprises. Within agro-industry, industry groups such as food products and paper products have substantially high level of fixed capital per worker reflecting higher level of technology infusion. In contrast to this, industry groups such as wood products , leather products and tobacco products have absolutely low level of fixed capi ta l per worker . The p ropor t ion of manufacturing expenses in total expenses is higher in the agro enterprises. Another troublesome aspect is that more t h a n th ree - fou r th of food p rocess ing DMEs (Directory manufac tur ing enterprises) operates for less t h a n seven months keeping large amount of fixed capital unutilised for major part of the year.

8. B e c a u s e of the low level of GVA arid profit and high proportion of manufacturing expenses, agro enterprises depend more on the 'putting out' system in order to generate income. It gets reflected in the high proportion of 'other' receipts (i.e., low proportion of manufacturing receipts) in the total receipts of these en terpr i ses . It is par t icular ly dominan t for the industry groups with agro industry that have low capital base and low level of itifusion of technology.

9. The access to credit and credit institutions is very poor in the case of agro enterprises. Moreover, the role of formal financial institutions has been absolutely negligible at least in terms of coverage of number of enterprises. It is ironical that even within the core manufacturing sector more than half the number of agro enterprises, that have taken loans, is covered by the informal sector for the purpose of financing. In terms of size of loans also the situation is very poor. The average size of loan outstanding for the agro enterprises works out to be merely Rs. 1,800 as against more than Rs. 8,000 in the non-agro enterprises. In tobacco, the average size of loan outstanding is as low as Rs. 200 per enterprise.

10. It is amply clear that poor access to credit particularly from formal f inancial i n s t i t u t i o n s h a s c o n s t r a i n e d the agro enterprises to invest in capital and technology and expand viably. This has led to low rate of re turn both in terms of GVA and profits. The agro enterprises, hence, are taking the

ix

help of putting out system in order to generate respectable profits. This phenomenon is more common in industry groups such as wearing apparel, textiles and tobacco processing. Looking at the overall situation, it can be said that a larger part of the agro enterprises are caught in the vicious circle of low credit - low surplus - high dependence on putting out system - low manufactur ing activities - low credit. It is extremely essential to break this vicious circle of the low level functioning of agro enterprises by the big push of credit interventions.

Policy Implication and Point of Action

11. Backward product ion l inkage of agro- indus t ry is much s t ronge r t h a n i t s forward p roduc t ion l inkage. But , as compared to all India, the forward production linkages of village level ag ro - indus t r i e s are weaker . The whole of agricultural production takes place in rural areas bu t the village level agro-industry is largely involved in secondary processing. At all India level, however, the agro-industry is mainly involved in primary processing. Therefore, the output of village level ag ro - indus t r i e s is more cons t ra ined by marketing since a smaller proportion of their output gets used as input in further manufacturing actiArities. This marketing problem has led to widespread prevalence of business service activities particularly among the smallest size group of OAvn account enterprises which is akin to the putting out system. This p h e n o m e n o n is leading to a vicious circle of low product ivi ty, low earn ings and low level of technology. Marketing infrastructure needs to be urgently provided to them—in the form of rural mandies, marketing cooperatives, largei" purchases by governments, etc. as suitable for various industries across different size classes.

12. At the village level, the agro-industries in comparison to the non-agro industries have lower level of gross value added and they have higher capital output ratio. This is indicative of the low level of efficiency of the village level agro-industries. It necess i t a tes the importance of absorbing more efficient technologies in agro-industries and reducing the gap even with village level non-agro industries.

13. The access to credit is very low in the case of village level agro-industries. Even the role of formal financial institutions

has been virtually negligible in terms of coverage. It is ironical that even in this core manufacturing activity, more than half of village level agro-industries access credit from informal sources. Although the average size of loan outs tanding is h igher in the case of f inancing by the formal f inancial institutions, the average loan outstanding is abysmally low in the agro-industries.

It is clear tha t the access to credit particularly from the formal financial institutions has constrained agro-industry to invest in fixed capital and new technologies and thus expand viably. The vicious circle of low credit - low surplus - high dependence on p u t t i n g out sys t em - low level of manufacturing activities needs to be broken by large-scale infusion of credit from the formal financial institutions.

14. The village level agro-industry does not come within the purview of any single Ministry. Consequently, it comes under the purview of multiple registration authorities. Because of this problem, only a fraction of the village level agro-industries are registered. An overwhelming proportion of the registered enterprises is registered with the village Panchayat. To infuse technology and credit in agro-industry, it is required to bring them under single registration authority and start a massive campaign to register village level agro-industries.

15. In spite of the initiation of several government programmes, lack of infrastructural facilities hinders the growth of agro-industries. These include electricity connection, power cut, availability of raw materials, transportation, etc. Infrastructural facilities need to be upgraded substant ia l ly for economic viability of these enterprises through widespread development of rural infrastructure.

16. We have already seen high prevalence of contract work in the agro-industries. To make such arrangement non-exploitative, some method needs to be evolved regarding the appropriate pricing of the products sold by these enterprises to larger establishments or merchants.

XI

CHAPTER I

INTRODUCTION

Agro-industry processes materials of p lant or animal origin by t ransformation and preservation through altering physical and chemical cheiracteristics and packaging. It has manifold contribution to economic development. It transforms raw material into finished products for consumption; constitutes a significant proportion of the developing countries' manufacturing production and exports and develops food system that provides the nutrients critical for the well-being of the expanding population.

The d e m a n d and necess i ty of ag ro -p rocess ing i n c r e a s e s a s agricultural production rises. Conversely, new processing activities can open u p new oppor tuni t ies to farmers , and t h u s , c rea te additional revenues for them. By creating new marke t for farm products, it can boost income of small /subsistent farmers. In the regional development context, it provides economic justification to build rural infrastructure. Agro-industry, playing a pivotal role in r u r a l i n d u s t r i a l i s a t i o n , provides s ignif icant and long- t e rm development stimulus to rural populace. When agro-industry creates backward demand, farm employment usually increases. This is indeed an important outcome, since agriculture is the primary employer in the developing countries. Further, it creates jobs in sectors like transportation, distribution and retail trade as well.

Agro-industry also plays a crucial role in the industr ia l isa t ion process of the developing countries. Although its importance declines as industrial isation advances, yet with rise in income and with increased urbanisation, the demand for more complex and diverse types of processed food increases . The value of commerce in processed foods exceeds that of basic agricultural commodities by several magnitudes (Handerson et al., 1998). Although bulk of this trade takes place in the developed countries and is dominated by a few multinational firms, the scope of subcontracting to these firms is enormous . Another impor tant contr ibut ion of agro- industry in manufacturing is its capacity to generate employment. It is generally found to be more labour intensive and less capital using and has low import content than the industry average in the developing countries. In this context, its significance in small-scale industry is particularly notable.

Definition of Agro-Industry

Agro-processing indus t r i e s by definition process mater ia l s of agricultural origin. Materials of agriculture may be of plant origin or can also include materials of animal origin. Similarly, processing may refer to primary processing only or it may Include secondary^ and tertiary^ processing as well. Therefore, from its narrowest to its broadest definition, the coverage of 'agro-processing industries' ranges from primary processing of materials of plant origin to all kinds of processing of materials of plant and animal origin.

The term agro-industry evoked a lot of confusion. The definition given by the Planning Commission had the widest coverage. The Planning Commission, with a view to, develop Agro-Industrial Corporation defined agro-industry in certain mcinner. Its main thrust was to increase agricultural productivity. Its criteria to characterise agro-industry were as follows : a) Encourages greater input into ag r i cu l tu re b) Leads to be t t e r p rocess ing and convers ion of agricultural commodities c) Ensures high return on processing of goods and d) Increases agricultural production. It classified agro-industry into agro-processing, agro-produce manufacturing, agro input manufacturing and agro service centre on the basis of these four criteria. It distinguished agro-produce manufacturing from agro-processing in the sense that the former produces new products in the form as distinct from agro raw materials being used (e.g. sugar, textiles etc.). NCAER (1965) looked at agro-industry as more of an organic link between agriculture and industry. It defined agro-based Indus t r i e s a s those which u s e agr icu l tu ra l raw mate r i a l s or manufacture products that farmers need for agricultural production.

In present day writings it is the agro-processing which Is largely considered as agro-industry. Austin (1981) defined agro-industry as an enterprise that processes agricultural raw materials including ground and tree crops as well as livestock. The degree of processing varies from the cleaning and grading of apples to the milling of rice, to the cooking, mixing and chemical alteration that create a textured vegetable food. According to him, considered agro-industry can roughly be categorised according to the degree of raw material transformed.

1. Secondary processing is further processing of products of primary agro-processing industries.

2. Tertiary processing is processing of by products of agricultural materials.

Srivastava (1989) considered agro-industry as an enterprise that processes bio-mass i.e. agricultural raw materials, which include ground and tree crops as well as livestock and fisheries, to create edible or usable forms, improve storage and shelf life, create easily transportable forms, enhance nutrition value and extract chemicals for others. It is divided into two parts: (a) agro-food industry, and (b) agro-nonfood industry. Processing includes processing of main products, processing of by products and extraction of chemical as well. Desai et al. (1991) divided agro-industry into food-processing and agro-related industry. They had a wider coverage of agro-industry in agro-related industry category in comparison with that of Srivastava.

UNIDO (United Nations Industrial Development Organisation) defined agro-industry as those industries which use raw materials from agriculture as main material from which manufactured goods are produced on a commercial scale (Satwani and Sharma, 1979). The term agriculture also includes fisheries and forestry. Agro-based industries are synonymous to agro-industries. It further defined agro-related industries/agro-allied industries as those which produce inpu t s to agr icu l ture or even mater ia l used for protect ion of agricultural products. In this study the UNIDO definition will be followed.

Recent research work by Chadha and Sahu (2003) also used same concepts. They defined agro-industry as subset of manufacturing tha t processes raw materials obtained from agriculture and its associated sectors such as animal husbandry, forestry and logging and intermediate products derived such as raw hides and skins for manufacture of leather and leather products.

Identification of Village Level Agro-Industries

Over the last two decades there has been a substant ia l shift of organised manufacturing from urban to rural areas because of space constraints, environmental concerns, other cost advantages, etc. However, more than nine-tenth of rural manufacturing workforce is in unorganised sector. Further , linkage of the rura l organised manufacturing sector to the other sectors of rural areas in relation to poverty reduction and indirect employment generation is quite weak.

So, the village level agro-processing uni ts of topical interest to NABAPiD belong to the unorganised sector (not registered under the

Factories Act of 1948) in rural areas. It includes three types of enterprises — own account enterprises (using only family labour), non-directory establishments (using hired labour but where total n u m b e r of workers is less t h a n 6) and larger sized directory establishments (which use hired labour and employ more than 5 workers).

Review of Past Studies

In the course of development through industrialisation, there is a general tendency towards increasing concentration of industry in the hands of a few large enterprises. The expansion of smaU enterprises brings variation in this process. The continuing play of opposite forces favouring large and small enterprises, whose relative strengths vary at different times and places particularly at different stages of economic development , t ha t de te rmines the size s t r u c t u r e of industry. Staley and Morse (1965), in their pioneering study, have explained several underlying patterns of advantages to small-scale i n d u s t r i e s . These p a t t e r n s embody the in te rac t ing effects of product ion costs, scale economies, market character is t ics and locational factors. They placed them in three distinct categories: (i) locational advantages for enterprises processing dispersed raw mater ia ls , having limited local market and with relatively high t r an spo r t costs ; (ii) process advantages where manufac tur ing operations can be separated, handicrafts and operations requiring simple assembly, mixing or finishing operations and (ill) market advantage factors for enterprises with differentiated product having low scale economies and selling in small total market. Ho (1980) tried to classify Korean and Taiwanese industries under these three categories of advantages and found tha t locational and process advan tages are most impor tan t for prevalence of smal l -scale industries. A further study of Korean economy over the years by him revealed that in course of development, the comparative advantages of small industries in locational factors (mainly transport cost) was giving way to process factor advantages. In case of both Korea and Taiwan he did not find the market advantage factor to be important.

Sundaram and Tendulkar (1988) discovered high differential of value added per worker not only between the rural households and the census sector but also between the rural and urban segments of the household indust ry at Identical two digit level in 12 out of 14 meaningful comparative cases for the year 1974-75. They gave several possible reasons (not empirically shown) for the coexistence of different segments with sizeable shares in the same two digit

code. First, different segments specialise in different product lines which does not get revealed in aggregated two digit code. Secondly, there could be product differentiation across different segments. However, this market advantage factor was not observed even in case Taiwan and Korea. Third, there are geographical segregation of product market and large transport cost which can be termed as locational advantages to small-scale sector. Fourthly, government policy favours small-scale sector by controlling raw material supply, imposing differential excise duties, providing scarce domes t ic / imported input at exclusive prices, etc. However, Little, Mazumdar and Page (1987) presented a different view. They found considerable differences in the employment size structure of six Indian states (namely Bihar, Haiyana, Madhya Pradesh, Punjab, Uttar Pradesh and West Bengal) even though they are subject to the same macro-economic and industrial policies. They, like Dhar and LydaU (1961), found considerable dearth of medium sized establishments (50-499 workers) in India as compared to Korea, Taiwan and the United States. But it was less so in Haryana and Punjab which could not be accounted by industrial mix favouring small units. They reasoned that Punjab and Haryana's rapid agricultural growth may be an important factor. In contrast, Papola (1987) did not observe any relation between agricultural growth and level and growth of rural industries' output and value added. He observed that in the faster growing areas, the households engaged in rural industries even on traditional varieties carried on their activities as sole occupation and they even used hired labour to a higher extent. This signifies gradual transformation of the informal sector into the formal sector. The major limitation of the above mentioned studies on India is tha t none of them, unlike Ho, has empirically examined the nature and significance of various factors in favouring/disfavouring small scale enterprises.

The s tudy by Sarka r (1995) indicated t h a t the Own Account Enterprises (OAE) the smallest size group in the unorganised sector, is disadvantageously positioned in terms of backward linkage, raw material concentration index and size of market factors. Their; ever diminishing advantages lie in dispersed raw material availability and sectors where processes are difficult to s tandard ise (i.e., wood products and furniture). Further, raw material concentration index and di rect backward l inkage are positively and significantly correlated. It signifies that agro-industries using larger proportion of mater ia l i npu t s also have added advantage in geographically concentrated availability of raw mater ia ls used in product ion. Whereas the advantages of factory sector lie in terms of larger

market, higher linkages and concentrated availability in raw material. Specialisation of agricultural production in different regions, higher income level by expanding size of market and better transportation facilities are likely to eat into the locational advantages that the OAE still possess.

Singh and Vyasulu (1990) observed that in the census sector (more than 49 workers in the factory sector) primary processing still dominates. Srivastava (1989) observed movements of agro-industries from mechanical based to chemical based processing bu t still mechanical-based processing dominates. So, on the whole, let alone rural village agro-processing, the whole of India's agro-industry is characterised by low value added. Apart from this, raw materials are usually the major cost component in agro-industries which are characterised by seasonality, perishability and variability (Austin, 1981). In such circumstances, concentrated availability of raw mater ials has distinct scale advantages to larger size group of industries in terms of prices, transportation and storage. Conversely, dispersed availability of raw materials may entail greater cost in terms of procurement and transportation for larger size groups as they require procurement of higher volume of raw materials. Size of market, another locational factor, is also important. Small sized market dispersed over a wide region is likely to be difficult for larger firms to serve on account of high transport and marketing costs.

In the post-liberalisation period, many micro-level studies have been done to examine the relative advantages of the small agro-processing uni ts ' vis-a-vls the larger ones and comparative advantages and per formances by them. Pal and Meena (2003), in a s tudy of randomly selected 12 Chilli processing units in Jodhpur district of Rajasthan after equally choosing from 3 size groups—small units (capacity upto 5 quintals), medium units (5-10 quintals per day) and large units (above 10 quintals per day) found that in the year 2000-01, maximum utilisation was in medium units (59 per cent), followed by large units (53 per cent) and small units (41 per cent). However, recovery rate of chilli powder was uniform in all size categories and all units were operating above breakeven capacity and earning profit. But the situation becomes quite different when there is shortage of raw materials. In a study of 30 mustard processing units, 10 from each size group of small, medium and large units on the basis of chamber size of processing un i t s in Bhind distr ict of Madhya Pradesh, Bakshi et al. (2003) observed that capacity of plant and total operating days of the plant are inversely related to each other revealing tha t the large sized processing plants operate for less

6

number of days as compared to the small sized processing plants due to shortage of raw materials (mustard). Pawar et al. (2003), in contrast reported economies of scale in rice processing. Although the cost per quintal of paddy processed is higher with the large units but because of their recovery is higher by about 8 per cent, the net re turns are much higher in case of the large units. The cost per quintal of paddy processed worked out to be Rs. 22, Rs. 23 and Rs. 27 for the small, medium and large processing units respectively, but the net returns per quintal of paddy processed worked out to be Rs. 8. Rs. 9 and Rs. 24 respectively. But it is not clear whether the recovery percentages with the small and medium uni t s are due entirely to scale diseconomies or to obsolete technology or some other constraints. This needs further research. Malliswari (1996) examined cost of four processing un i t s of different sizes which produce mango pulp for independent marketing. It was found that except for raw material, all the other costs are higher per unit in other concerns in relation to the smallest scale of product ion, thereby indicating tha t there is a subs tan t ia l element of fixed expenditure under each head and that economies of scale are not reaped at this level. On the other extreme, the large scale concern which had highly sophisticated machinery and organisation, had incu r red more expenses on deprec ia t ion and admin i s t r a t i ve overheads. Consequently, their processing costs were much higher. She also found substant ia l cost advantages in pulp processing activities at the place of production of raw material which are discussed by Staley and Morse (1965) as locational advantages of processing dispersed raw materials at source. She attributed it to reduction of transport cost as well as spoilage of fruits in transit. Transport cost gets halved because instead of treinsporting 2 tons of mangoes, only 1 ton of pulp needs to be transported when processed at origin (two tons of mangoes make one ton of pulp). Further, shelf life of mango pulp gets increased to 10 to 12 months as against shelf life of mango of hardly one month.

Objectives of the Paper

(i) To examine the financial s t a tus as also economics of the investment and assess forward and backward linkages of agro-processing units.

(ii) To analyse relative position of village level agro-processing units in oversdl framework of rural non-farm sector.

(lii) To estimate the strength of inter-relationship/inter-linkage with inpu t supplying agr icul tural a n d / o r pr imary agro-processing sectors.

(Iv) To study the physical and financial constraints faced by the village level un i t s as also government policy inhibi t ing/ promoting development of the village level agro-processing units.

(v) To attempt to examine factors influencing entrepreneurship in running the units, capacity utilization and viability of such uni ts and suggest the required marketing framework with special reference to penetration and enlargement of market share.

Chapter Plan

The pape r is divided into six c h a p t e r s . The first chap te r is introduction. It covers definition and identification of the village level agro-processing units. Further it undertakes a literature review of relative advantages of small enterprises vis-a-vis the large units. It also specifies the objectives of the study and chapter plan. The second chapter deals with the relative position of village level agro-units. It is analysed in two parts: First, it analyses the position of manufacturing in rural non-farm sector. Secondly, it explains the importance of agro-units in the manufacturing sector. The third chapter deads with backward and forward linkages of agro-processing sectors and their relative strength. The product of agro-processing sectors mostly consti tutes the basket of final demand segment. Therefore, the linkage of agro-processing that really matters is that of backward production linkage. So, this chapter largely deals with backward production linkage of agro-industry.

The fourth chapter covers financial health of agro-units and their access to credit market. These are examined in terms of various physical and financial ratios and composition of outstanding and fresh loans in terms of source of loans. The fifth chapter covers broadly the constraints faced by agro-units. The government policy towards rural industrialisation is also discussed here. The last chapter presents summary of earlier chapters. It also provides broad policy suggestions regarding further development of agro-processing units.

8

CHAPTER II

RELATIVE POSITION OF VILLAGE LEVEL AGRO-INDUSTRIES

In this chapter we discuss the relative position of village level agro-industries. It is discussed in two stages. First we discuss the relative position of manufacturing in the rural non-farm sector. Secondly, we ana lyse t he pos i t ion of ag ro - indus t ry in t h e village level manufacturing. Lastly, we also examine the composition and size distribution of the village level agro-Industries.

Relative Position of Manufacturing in Rural Non-farm Sector

In the rural areas, most people work in agriculture. The dependence has mcirginally reduced from over four-fifths of all rural workers to little over three-fourths of all rural workers in the span of last two decades of twentieth century (1983-84 to 1999-2000). However, manufacturing is the largest employer after agriculture. The share of manufacturing in rural employment has gone up in the last two decades (table 2.1). The rise is observed during the period 1983-84 to 1993-94 (6.8 to 7.0 per cent) and in between 1993-94 and 1999-2000 (7.0 to 7.4 per cent). But the rise is of small magni tude whereas the share of rural non-farm employment in total rural emplojmient went up substantially from 18.55 per cent in 1983 to 23 .87 per cent in 1999-2000 . As a r e su l t , the s h a r e of manufacturing in rural non-farm sector showed continuous decline from 36.05 per cent in 1983 to 32.51 per cent in 1993-94 and further to 30.98 per cent in 1999-2000. The other segments of rural non- fa rm sec tor have increased the i r s h a r e a t the cos t of manufacturing sector. The most d5niamic segments of rural non-farm sec tor a re t r ade and cons t ruc t ion in t e r m s of employment absorption. The service sector, another large segment of rural non-farm sector, also shows substantial fall in its share of rural non-farm employment in the last one decade. It is largely due to the fall in the share of community and social services (not shown in table 2.1). The rura l t ranspor t segment, a l though small in te rms of volume of employment absorption, has shown substantial dynamism in incremental employment absorption in the last two decades.

This becomes clearer when one looks at the growth of employment in the last two decades (Table 2.2). The growth of total rura l employment fell substantially in the last two decades. In between

1983 and 1993-94, the growth rate of rural employment was 1.75 per cent per annum but it got more than halved in the subsequent period to 0.66 per cent per annum in between 1993-94 and 1999-2000. It is largely due to substantial fall in employment absorption from the earlier period (1983 to 1993-94) of 1.38 per cent to only 0.18 per cent in the later period (from 1993-94 to 1999-2000) in agricultural sector. Even employment growth in rural non-farm sector showed reduction from 3.23 per cent in the earlier period to 2.31 per cent in the later period. A similar picture is observed in the manufacturing segment where the growth rate has fallen from 2.14 per cent to 1.78 per cent per a n n u m . But in contras t , agro-processing manufactur ing sector showed a rise of employment growth from 1.45 per cent to 2.16 per cent in the post liberalisation period as compared to the pre liberalisation period. The other rural non-farm sectors that showed buoyancy in growth in the later period are construction and transport segments.

Table 2.1 : Usual Status Workers in Rural Areas (UPSS)

Sr. No.

Sectoral Share of Workers in Rural Areas

Sectoral Share of Workers in

Non-farm

Sector 1983 1993-94

1999-2000

1983 1993-94

1999-2000

1 Agriculture 81.45 78.46 76.13

2 Mining & quarrying 0.49 0.49 0.50 2.64 2.25 2.07

3 Manufacturing 6.80 7.00 7.39 36.65 32.51 30.98

4 Utilities 0.13 0.23 0.13 0.68 1.06 0.55

5 Construction 1.65 2.38 3.31 8.89 11.04 13.88

6 Trade 3.48 4.28 5.13 18.78 19.89 21.47

7 Transport 1.11 1.45 2.12 5.98 6.73 8.88

8 Services 4.89 5.71 5.29 26.37 26.52 22.16

Total 100 100 100 100 100 100

Source : Chadha (2002).

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Table 2.2 : Growth of Rural Workers (UPSS) Per Annum

(in percentages)

Sr.No Sector 1983-84 to 1993-94 1993-94 to 1999-2000

1 Agriculture 1.38 0.18 2 Mining 3.84 -2.28 3 Manufacturing 2.14 1.78

a. Agro 1.45 2.16 b. Non-agro 3.58 1.03

4 Utilities 4.7 -5.65 5 Construction 5.18 6.43 6 Trade 3.72 1.81 7 Transport 4.05 7.29 8 Finance Ser. 5.99 4.51 9 Commu. Ser. 3.13 0.32

(2 to 9)Non-fann 3.23 2.31 Total 1.75 0.66

Source: Chadha (2002).

Let US now examine what contribution the manufacturing sector makes to the r u r a l n e t domest ic p r o d u c t (NDP). Unlike in employment share where the share of agriculture has not fallen substantially, in the case of rural NDP the share of agriculture has fallen substantially from 64 per cent to 54 per cent in the last two decades (Table 2.3). In the rural non-farm sector, the share of manufacturing has fallen by eight per cent from 26 to 18 per cent. The fall is much more than the fall in the share of employment. This has happened in spite of substantial rise in share of registered manufacturing in the eighties. The reason is substantial fall in the share of unregistered manufacturing from about 17 to 7 per cent in the eighties. In the nineties, the unregistered manufacturing sector hardly showed any fall in its share in rural NDP. It gets reflected in substantial rise in gross value added (GVA) of the rural unorganised manufac tu r ing . The gross value added of r u r a l uno rgan i s ed manufacturing (substantial portion of which is accounted by the unregistered sector) has gone up from Rs. 12,995 crores in 1989-90 to Rs. 18,432 crores in 2000-01, a rise of almost 50 per cent in a span of ten years that covers little more than the period of economic liberalisation (see Table 2.8). This is in sharp contras t to the marginal rise from Rs. 11.775 crores in 1984-85 to Rs. 12,995 crores in 1989-90. This has led to substantial fall in the share of the rural unregistered manufacturing in rural non-farm NDP from 16.86 per cent to 6.84 per cent from 1980-81 to 1993-94.

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Table 2.3 : Sectoral Share in Rural NDP and Rural Non-farm NDP

Sectoral Share of Rural NDP

Sectoral Share of Rural Non-farm NDP

Sr. No.

Sector 1980-81

1993-94

1999-2000

1980-81

1993-94

1999-2000

1 Agriculture 64.36 57.00 54.41

2 Non-agriculture 35.64 43.00 45.59

3 Mining & quarrying 1.24 2.59 2.39 3.48 6.02 5.24

4 Manufacturing 9.16 8.16 8.13 25.70 18.98 17.83

5 i) Registered 3.15 5.22 5.13 8.84 12.14 11.25

6 ii) Unregistered 6.01 2.94 3.00 16.86 6.84 6.58

7 Utilities 0.56 0.88 1.34 1.57 2.05 2.94

8 Construction 4.05 4.61 4.99 11.36 10.72 10.95

9 Trade 6.68 7.77 6.94 18.74 18.07 15.22

10 Transport 1.32 3.41 4.17 3.70 7.93 9.15

11 Services 12.63 15.58 17.63 35.44 36.23 38.67

Source: Chadha (2002).

The importance of unorganised manufacturing can also be observed from the informal sector survey of 1999-2000 conducted by the National Sample Survey Organisation (NSSO). The informal sector leaves out the more dynamic enterprise segments of the rural non-farm unorgan i sed sector which are regis tered unde r var ious authorities other than that of the factories' Act. The informal sector survey shows t h a t in the informal r u r a l non-farm sector , manufacturing is even more dominant. It shows that 38 per cent of all non-agricultural enterprises, 44 per cent of all workers and 33 per cent of gross value added is contributed by the manufacturing sector (see tables 2.4a, 2.4b and 2.4c). The informal manufacturing sector employs more workers per enterprises, but its value added per worker is much smaller than that of the other non-agricultural enterprises (Table 2.4d). But in terms of share of emoluments, the share of manufacturing sector is higher than the share of workers in the rural non-farm sector, thereby showing higher share of paid workers in manufacturing (Table 2.4e). The reason for lower value added pe r worker may lie in the fact t h a t the s h a r e of manufacturing in aggregate value of assets owned and hired is much smaller than the share in employment signifying smaller per fixed capital per worker.

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At a more disaggregated level, when enterpr ises are analysed separately for own account enterprises (OAE) emplojring only family labour on long-term basis and establishments emplojmig at least one hired worker in the informal sector, we get quite interesting result. The share of manufacturing OAE in non-agricultural OAE in number of enterprises is 38 per cent and in number of workers is 44 per cent, thereby showing more number of workers employed per OAE in manufacturing than in other non-agricultural OAE. But the share of manufac tur ing OAE in gross value added is only 29 per cent resulting in workers productivity of Rs. 8,963 as compared to Rs. 13,443 in all non-agricultural OAE, thereby showing a difference of more t h a n 50 per cent . But in e m o l u m e n t s , t he s h a r e of manufacturing OAE in non-agricultural OAE is substantial (49 per cent).

As compared to OAE, in the informal sector the dominance of manufacturing establishments in non-agricultural sector is more prominent . The share of manufacturing establ ishments in non-agricultural es tabl ishments is 43 per cent, share in number of workers is 50 per cent and in value added is little less at 46 per cent . The gap of manufac tur ing es t ab l i shments with all non-agricultural enterprises in workers productivity is much less as compared to the OAE segments — a difference of hardly 10 per cent with emoluments per worker in manufacturing establishment almost at par with all non-agricultural establishments (because share of manufacturing establishments in non-agricultural establishments in number of workers and emoluments are almost the same). It gives credence to the argument of Chadha and Sahu (2003) tha t the whole lot of additional rural OAME that came up post 1994-95 in the agro-processing sector, being largely manned by part-time family workers, can be seen in terms of employment setback suffered by other sectors of rural economy; most pointedly by agriculture and allied sec to r s . It can also be observed from our ana ly s i s of UPSS (Usual principal and subsidiary status) workers tha t the growth of emplojmient in agro-based industries improved from 1.45 per cent during 1983 to 1993-94 to 2.16 per cent during 1993-94 to 1999-2000 while tha t of the noriSagricultural sector declined s u b s t a n t i a l l y from 3 .23 per cen t to 2 .31 per cen t in t h e corresponding period.

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Table 2.4a : Number of Enterprises

(in thousand) Rural

OAE Establish. All

Manufacturing 9,005 608 9,613

Non-agri. 23.656 1,411 25,068

% Manu. in non-agri 38.07 43.10 38.35

Table 2 .4b : Number of Workers

(in Lakh)

Rural

OAE Establish. All

Manufacturing 148.7 28.3 176.9

Non-agri. 341.4 56.7 398.1

% Manu. in non-agri. 43.56 49.91 44.44

Table 2 .4c : Gross Value Added

(in Crores)

Rural

OAE Establish. All

Manufacturing 13.325 6,413 19,738

Non-agri. 45,897 13,848 59,746

% Manu. in non-agri. 29.03 46.31 33.04

Table 2 .4d : Gross Value Added Per Worker

(in Rs.)

Rural

OAE Establish. All

Manufacturing 8.963 22,699 11,157

Non-agri. 13,443 24,442 15,008

% Manufacturing 66.67 92.87 74.34

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Table 2.4e : Aggregate Value of Emoluments

(in Rs. Crores)

Rural

OAE Establish. All

Manufacturing 278 2,511 2,790

Non-agri. 572 5,061 5,634

% Manufacturing in non-agri. 48.64 49.62 49.52

Table 2 .4f : Aggregate Value of Asse t s Owned and Hired

(in Rs. Crores)

Rural

OAE Establish. All

Manufacturing 13,546 7,970 21,516

Non-agri. 46,970 21,547 68.517

% Manufacturing in non-agri. 28.84 36.99 31.40

The importance of manufacturing sector in rural non-agricultural sector can be analysed from other sources as well. The Economic Census of 1998 provides information on enterprises covering all size groups of enterprises from OAE, NDE and DE to factory (organised) sec tor . One can cull ou t Informat ion from it re la ted to t h e unorganised sector which is the focus of our study (see tables 2.5a and 2.5b) . I ts l imi ta t ion is t h a t i ts coverage in t e r m s of c h a r a c t e r i s t i c s covered is l imited. It covers b road ly two characteristics useful for our study, namely number of enterprises and employment. In number of enterprises characteristic, the share of manufacturing is 24 per cent as opposed to 38 per cent in the case of the informal sector survey of 1999-2000 as mentioned in the earlier section. It shows that the presence of registered enterprises is relatively more numerous in other non-farm enterprises apart from manufacturing. The share of manufacturing in informal sector is equally strong both in OAE and establishment segments. But in case of all enterprises covered under the economic census, the picture is quite different. In the OAE, the share of manufacturing enterprises in the informal sector is 38 per cent as compared to 26 per cent in the economic census. In the establishment segment, the share of manufacturing enterprises is 43 per cent in the informal sector survey compared to the pa l t ry 20 per cen t in case of t he unorganised sector. It goes to reflect that establishments in other

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non-agricultural sector apart from manufacturing are much more likely to be registered. With regard to the emplo5anent characteristic, the difference of sha re of manufactur ing is comparatively less thereby confirming the earlier assertion that the manufacturing units register only when it reaches some threshold size in te rms of employment which is not true for the other non-farm enterprises.

Table 2.5a : Number of Enterprises by Tjrpe

(in 000')

Rural

OAE NDE DE NDE+DE Total

Manufacturing 2,761 531 2 2 4 7 5 5 3,516

Non-agri. 10,714 3,188 6 0 5 3,793 14.507

% Manufacturing in non-agri. 25.77 16.66 37.02 19.91 24.24

Table 2.5b : Number of Employment by Type (in 000')

Rural

OAE NDE DE NDE+DE Total

Manufacturing 5.007 1,431 5,209 6,640 11,647

Non-agri. 15,812 7,269 10,687 17,956 33,768

% Manufacturing in non-agri. 31.67 19.69 48.74 36.98 34.49

Position of Village Level Agro-Processing in Rural Manufacturing

The role of village level agro-processing uni ts is analysed on the basis of the several unorganised manufacturing sector surveys undertaken by the National Sample Survey Organisation (NSSO) over the last two decades. These surveys were undertaken in the year 1984-85 (40* round), 1989-90 (45* round), 1994-95 (51*' round) and 2000-01 (56"^ round). These surveys cover only the unorganised manufacturing sector in the sense that these are not registered under the factories' Act. However, most of the bigger enterprises covered by these surveys are registered under several bodies like Village Panchayat, SIDO (Smadl Industrial Development Organisation), KVIC (Khadi Village Udyog Commission), etc. The factories' Act necessitates that the manufacturing establishments emplo3ang 10 or more workers using power or 20 or more workers not using power have to be registered under It. The enterprises covered under these sample surveys can be enterprises with only family labour (own

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account enterprises) or es tabl ishments employing less t h a n 10 workers with power or less than 20 workers without power. The village level en te rpr i ses in th is s tudy are comprised of t hose enterprises which are in the unorganised segment of manufacturing.

Nature of Operation

The village level agro-industries are mostly permanent in nature . More t h a n n ine - t en ths of all village level agro- indus t r i es a re perennial. But in the largest sized village level industries, i.e. DME, a substantial segment is seasonally operated, i.e. more than one-third of them are seasonal. A similar phenomenon is observed in the case of non-agro industries as well.

Table 2.6 : Nature of Operation

Nature of Operation

Type Perennial Seasonal Casua l

Agro GAME 91.91 6.80 1.29 Agro

NDME 91.01 8.89 0.10

Agro

DME 61.07 38 .93 0.01

Agro

Total 91 .42 7.36 1.22

Non-agro GAME 89 .93 8.71 1.36 Non-agro

NDME 92 .90 6.78 0 .32

Non-agro

DME 62.80 37.11 0 .08

Non-agro

Total 88 .76 10.02 1.22

Manufactur ing Total 90 .98 7.80 1.21

Registration

In the case of registered units the picture is jus t the opposite. The number of registered uni ts in village level agro-industries is very small. Hardly 5 per cent of all agro-industries is registered. But the share of registered units is almost negligible in case of OAME (hardly 3 per cent). But in the case of NDME and DME, the sha re of registered units is substantially higher. In the case of NDME, one-fourth of all units are registered whereas in the case of DME, it is more than one-third. However, in the case of both these categories, the share of registered units is much higher in the case of non agro-industr ies . However, as compared to NDME and DME in agro-processing, in non-agro industries, the percentage of enterprises registered is much larger, so much so that in the DME of non agro-processing, more than half of the enterprises are registered.

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Table 2.7: Registration Pattern

Registered or not

Type Yes No

Agro GAME 3.41 96.58 Agro NOME 25.57 74.40

Agro

DME 38.63 61.36

Agro

Total 4.99 95.01

Non-agro GAME 2.79 97.21 Non-agro

NOME 31.32 68.68

Non-agro

DME 58.73 41.24

Non-agro

Total 7.89 92.11

Manufacturing Total 5.47 94.53

Importance of Agro-Processing in Village Level Manufacturing

Let us now examine the size of village level agro-processing units in relation to the village level manufacturing activities (see table 2.8). The number of village level agro-processing units is large, counting over 115 lakhs even in 1984-85. The absolute number of these enterprises got substantially reduced in 1989-90 to 94 lakh units and further to 75 lakh uni ts in 1994-95. Only in 1999-2000 it showed substantial increase to 100 lakh units. Similar changes are observed In terms of other characteristics like emplo5mient and gross value added. S tar t ing with 210 lakh employment in 1984-85, employment in the agro-process ing i n d u s t r y got r educed substantially to 157 lakh units in 1994-95 thus loosing 53 lakhs emplo3mient in between 1984-85 and 1994-95. Only during 1994-95 to 2000-01 , the agro-processing uni ts were able to add 32 lakh employment in a period of six years. In the case of gross value added, the reversal of fortune of agro-processing units is even more spectacular in between 1994-95 and 2000-01. The gross value added of the village level agro-processing units went up from Rs. 8,014 crores to Rs. 13,319 crores, a rise of more than 50 per cent whereas in between 1984-85 and 1994-95 it got reduced from Rs. 9,965 crores to only Rs. 8,014 crores.

Now let u s see how agro-processing uni ts fared in the last two decades in relation to the non-agro processing manufacturing units. This is examined in terms of the share of agro-processing in the

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manufacturing sector (see tables 2.9a and 2.9b). In the last two decades, the share of agro-processing units in terms of number of enterprises showed marginal decline, in terms of employment its share fell by almost 6 per cent but in terms of gross value added, its share fell down substantially by 12 per cent. But in the post liberalisation period, that is in between 1994-95 and 2000-01, the share of agro-processing units in total manufacturing went up in number of units and employment but showed marginal decline in t e rms of g ross va lue added . Consequen t ly , t he per worker productivity gap has substantially widened in favour of non-agro processing un i t s in 2000-01 . In the year 1984-85, per worker productivity was marginally higher in agro-processing at Rs. 4,745 as compared to Rs. 4,567 in the non-agro processing sector. But over the years the growth of non-agro processing units has been much faster than that of agro-processing in per worker productivity and in the year 2000-01, the productivity gap was substantial . The per worker productivity of agro and non-agro processing are at present at Rs. 7,096 and Rs. 9,803 respectively with a gap of about 40 per cent.

One of the important reasons for this productivity gap can be found in the substant ial difference in fixed capital per worker between agro-processing and non-agro processing workers (see table 2.10). The value of fixed assets per worker in agro-processing in the year 2000-01 was only Rs. 12,249 as opposed to Rs. 19,120 in the case of non-agro processing — a difference of more than 50 per cent. When one looks at the breakdown of fixed assets , a substant ia l difference is found not only in fixed assets owned per worker but also more in fixed assets hired per worker. The value of assets hired per worker in non-agro processing is more than double than that of agro-processing. When one looks at addition to the fixed assets in the last one year, i.e. incremental assets in the last one year, the difference turns out to be even substantially higher. The addition to the fixed assets per worker turned out to be Rs. 1,306 in case of non-agro processing but in case of agro-processing it is only Rs. 294 — a gap of more than five times. If these trends are not reversed, in future the gap in worker productivity between agro and non-agro processing is likely to be even much higher. A similar picture can be seen in the case of loans outstanding (see table 2.11). Lx>an amount outstanding per enterprise in agro and non-agro processing were Rs. 1,879 and Rs. 8,250 respectively and that of outstanding interest were of the magnitude of Rs. 283 and Rs. 1,491 respectively. So the non-agro processing units per enterprise had taken loEins nearly five times than that of the agro-processing units. The substantial part of

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this difference can be found in size composition of agro-processing and non-agro processing units, which will be discussed in the next section.

However, even with such dismal performance, the agro-processing units still occupy dominating position in rural manufacturing. In 2000-01, more than 80 per cent of all manufacturing units were agro-processing units, little less than 80 per cent employment of manufacturing sector was in the agro-processing segment and little over 70 per cent all gross value added originated in the agro-processing units.

Table 2.8 : Number of Enterprises, Employment and GVA in Rural Unorganised Manufacturing

Year

Number of Enterprises**

Employment* * Gross Value Added*

Year Agro Non-agro

Manu. Agro Non-agro

Manu. Agro Non-agro

Manu.

1984-85 115 19 134 210 40 250 9,965 1,810 11,775 1989-90 94 19 113 184 49 233 9,944 3,051 12,995 1994-95 75 21 96 157 51 208 8,014 2,960 10,973 2000-01 100 20 119 188 52 240 13,319 &,114 18,432

* At constant 1993-94 prices In thousand rupees. ** Number of enterprises and employment figures are in lakhs and gross value

added figures are in Rs. Crores.

Table 2.9a : Share of Agro and Non-agro Enterprises in Rural Unorganised Sector

Year Enterprises Employment Gross Value Added

Year Agro Non-agro Agro Non-agro Agro Non-agro 1984-85 85.61 14.39 84.12 15.88 84.63 15.37 1989-90 82.83 17.17 78.90 21.10 76.52 23.48 1994-95 78.10 21.90 75.44 24.56 73.03 26.97

2000-01 83.42 16.58 78.25 21.75 72.26 27.74

Table 2.9b : Per Worker Productivity (In Rs.)

Year Agro Non-agro

1984-85 4 ,745 4 ,567

1989-90 5.405 6,203

1994-95 5,103 5.790

2000-01 7,096 9 ,803

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Table 2.10 : Fixed Assets and Addition to Fixed Assets in 2000-01

Industry Fixed Assets

Rs. Crores

Fixed Assets

per worker

Addition to Fixed Assets (Rs. Crores)

Addition to Fixed Asset per worker

Fixed Assets Hired per Worker (in Rs.)

Agro 22,991 12,249 551.78 294 1,545

Non-agro 9,974 19,120 681.06 1,306 4,362

All 32,965 13,743 1232.84 514 2,157

Table 2.11 : Outstanding Loans per Enterprise in 2000-01

Indus t ry Loans Outs tand ing Indus t ry

Amoun t In teres t

Agro 1,879 2 8 3

Non-agro 8,250 1,491

Ml 2 ,935 4 8 3

Composition of Agro-processing Sector

The composition and size distribution of the agro-processing sector is analysed in t e rms of the National Sample Survey' 56''' round conducted in the year 2000-01 . There are several r easons for undertaking this analysis on the basis of the latest available data. First, as the purpose of this paper is to examine the s ta tus and potential of agro-processing sector, the latest round data shows a s ignif icant b r e a k from the ear l ier r o u n d s on u n o r g a n i s e d manufacturing. Secondly, the latest round data is based on NIC (National Industrial Classification) of 1998. It is quite different in a very fundamental manner from the earlier NIC classification of 1987. At the two digit level of NIC 1998 classification, ginning, cleaning and bciiling of cotton {code 01) has been shifted from agriculture and allied sector to manufacturing sector for the first time. Secondly, a substantial chunk of the group of manufacture of wearing apparel, dressing and dyeing of fur products (code 18) was not covered under the manufacturing sector in the earlier NSS rounds. The substantial chunk of this group is consists of tailoring activities which were earlier covered under the service sector. In case of analysing over the years, this important sub-sector of agro-processing activities that generate substantial portion of employment and gross value added

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had to be left out. In the subsequent analysis of this section we analyse size composition of agro-processing in terms of the three size groups as defined below:

Code Description Definition

GAME Own Account Manufacturing Enterprises

An enterpr ise , which is r u n wi thou t any hi red worker employed on a fairly regular basis

NOME Non-Directory Manufacturing Establishments

An establishment employing less than six workers (household and hi red workers together)

DME Directory Manufacturing Establishment

An establishment employing six or more workers (household and hired workers together)

The composi t ion of agro-processing is analysed in t e rms of following NIC 1998 classification:

NIC 1998 Code

Activity

01405 Cotton ginning, cleaning and bailing

15 Manufacture of food products and beverages

16 Manufacture of tobacco products

17 Manufacture of textiles

18 Manufacture of wearing apparel, dressing and dyeing of fur

19 Tanning and dressing of leather, manufacture of footwear and other leather items

20 Manufacture of wood and of products of wood and cork, except furniture, manufacture of straw and plaiting materials

21 Manufacture of paper an paper products

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The cotton ginning, cleaning and bailing unit (code 01405) in agro-processing sector is a very small segment of the agro-processing sector. Apart from it, leather processing industry (code 19) and manufac tu re of paper and paper p roduc t s (code 21) are also comparatively small segments of all rural agro-processing units in the sense tha t the share of all these three indust ry groups in number of enterprises, employment, value added and fixed capital is at best around one per cent each in the agro-processing industry.

Therefore, out of 8 industry groups at the two digit level, only 5 industry groups have substantial presence in the agro-processing industry.. So, our analysis largely evolves around these five industry groups. Out of 99.5 lakh agro-processing units , on-sixth each is accounted by tobacco products (code 16), textiles (code 17) and wearing apparels (code 18). But the most prominent industry groups are food products (code 15) and wood & wood products (code 20) accounting for little less than one-fourth of all agro-processing enterprises each (see tables 2.12a and 2.12b). But food products dominate in NDME and DME combined, accounting for more than 40 per cent of all establishments whereas OAME is more evenly spread out, but more than one-fifth of all OAME enterprises are in food product industry group. Tobacco products and wood & wood products are more numerous in OAME compared to NDME and DME but in contrast food products and textile products are relatively more numerous in establishments.

Table 2.12a : Number of Manufacturing Enterprises (Rural)

Sr. No. Indust ry {NIC 1998 code) OAME NDME DME ALL

1 Cotton ginning etc. 0145(0145) 1,804 222 66 2 .093

2 Food produc ts (15)15 2,104,292 190,597 65,804 2 ,360 ,693

3 Tobacco products (16)16 1,615.877 12,699 19,593 1.648,169

4 Textile p roduc ts (17)17 1,552,011 92,178 45 ,255 1,689.444

5 Wearing appare l (18)18 1,545,661 120,099 5,458 1,671,218

6 Tanning & leather p roduc ts (19) 19 87 ,631 3,172 681 91 ,484

7 Wood & wood produc ts (20)20 2,402,384 56,227 7,819 2 ,466,430

8 Paper & paper products (21)21 25 ,243 407 599 26 ,250

9 Agro 9,334,903 475,601 145,275 9,955,781

10 Non-agro 1,723,345 153,880 101,579 1,978,802

All 11,058.248 629,481 246,854 11,934,583

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Table 2.12b : Industry Composition of Manufacturing Enterprises (Rural)

Sr. No. Indus t ry (NIC 1998 code) GAME NDME DME ALL

1 Cotton ginning etc. (0145)01405 0.02 0.05 0.05 0.02

2 Food products (15)15 22.54 40.07 45.30 23.71

3 Tobacco products (16)16 17.31 2.67 13.49 16.55

4 Textile p roduc t s (17)17 16.63 19.38 31 .15 16.97

5 Wearing apparel (18)18 16.56 25 .25 3.76 16.79

6 Tanning & leather p roduc ts (19) 19 0.94 0.67 0.47 0.92

7 Wood & wood products (20)20 25.74 11.82 5.38 24.77

8 Paper & paper p roduc ts (21)21 0.27 0.09 0.41 0.26

9 Agro 84.42 . 75.55 58.85 83.42

10 Non-agro 15.58 24.45 41.15 16.58

All 100.00 100.00 100.00 100.00

A similar scenario can be observed in the case of manufacturing emplojnment as well (see tables 2.13a and 2.13b). The presence of textiles products in the employment characteristic is more prominent as compared to the number of enterprises in OAME as well as in establishments (NDME & DME combined) segment. But food product is more prominent in the employment characteristic as compared to the number of enterprises only in the case of OAME. Establishments (NDME & DME combined) are less prominent in employment c h a r a c t e r i s t i c a s compared to the n u m b e r of en t e rp r i s e s characteristics. Similarly, a mixed picture can also be observed in the case of wood and wood products.

Table 2.13a : Number of Manufacturing Employment (Rural) Sr.No. Indus t ry (NIC 1998 code) OAME NDME DME ALL

1 Cotton ginning etc. (0145)01405 2,486 586 1,708 4 ,780

2 Food p roduc t s (15)15 4 ,016,224 564,547 590,291 5,171,062

3 Tobacco p roduc t s (16)16 2 ,467 ,015 48 ,382 226,309 2 ,741,706

4 Textile p roduc ts (17)17 3,086,722 321 ,468 464 ,676 3 ,872,866

5 Wearing appare l (18)18 1,946,360 319,068 39,838 2 ,305,265

6 Tanning & leather p roduc ts (19) 19 117,712 9,971 6,120 133,803

7 Wood & wood products (20)20 4 ,247.270 172,879 58,683 4,478,832

8 Paper & paper p roduc ts (21)21 52,185 1,771 7,086 61,042

9 Agro 15,935,974 1,438,672 1,394,711 18,769,356

10 Non-agro 3 ,211,178 494,214 1,510.953 5,216,346

All 19,147,152 1,932,886 2 ,905,664 23,985,702

24

Table 2.13b :Industry Composition of Manufacturing Employment (Rural)

Sr.No. Indus t ry (NIC 1998 code) GAME NDME DME ALL

1 Cotton ginning etc. (0145)01405 0.02 0.04 0.12 0 .03

2 Food p roduc t s (15)15 25.20 39.24 42.32 27 .55

3 Tobacco p roduc t s (16)16 15.48 3.36 16.23 14.61

4 Textile p roduc t s (17)17 19.37 22.34 33.32 20 .63

5 Wearing appare l (18)18 12.21 22 .18 2.86 12.28

6 Tanning & leather products (19)19 0.74 0.69 0.44 0.71

7 Wood & wood p roduc t s (20)20 26 .65 12.02 4 .21 23 .86

8 Paper & paper p roduc ts (21)21 0.33 0.12 0.51 0 .33

9 Agro 83.23 74.43 48.00 . 78 .25

10 Non-agro 16.77 25 .57 52.00 21 .75

All 100.00 100.00 100.00 100.00

But in the case of gross value added, the picture is somewhat different. The food products sector dominates the whole of agro-processing industry in this characteristic (Tables 2.14a and 2.14b). This is true for OAME where in terms of number of enterprises and employment, wood & wood products had more prominent presence. In gross value added, the food products account for nearly one-third of all value added generated in the rural agro-processing industry. Their dominance is even stronger in NDME and DME size groups but they had dominant position even in the number of enterprises and employment characteristics. Another important aspect is that although in number of enterprises and employment, OAME were way ahead of DME in agro-processing and all large industry group, in gross value added in at least in paper and paper products, 63 per cent of all value added is generated by DME. Even in the large industry group like textile products DME contributed nearly one-fourth of all yalue added generated in it whereas in t e rms of employment creation the share of DME textile products is hardly 12 per cent.

25

Table 2.14a : Annual Gross Value Added

(in Rs. crore)

Sr.No. Industry (NIC 1998 code) GAME NOME DME ALL

1 Cotton ginning etc. (0145)01405 2 1 3 6

2 Food p roduc t s (15)15 4.389 1,014 952 6,356

3 Tobacco produc ts (16)16 1,627 71 198 1,896

4 Textile p roduc t s (17)17 2,236 445 891 3,571

5 Wearing appare l (18)18 2,029 531 60 2,619

6 Tanning & leather produ cts (19) 19 170 26 15 212

7 Wood & wood products (20)20 3.158 406 229 3,794

8 Paper & pape r p roduc ts (21)21 16 5 3 4 54

9 Agro 13,627 2,498 2,382 18,508

10 Non-agro 3,189 1,194 3,781 8,164

All 16.816 3.692 6,163 26,671

Table 2.14b : Distribi Added Aero

ition oi ss Indu

' Annual C stry Grou

irross Vail P

l e

Sr.No. Indus t ry (NIC 1998 code) GAME NDME DME ALL


2 Food produc ts (15)15 32.21 40 .60 39 .98 34.34

3 Tobacco produc ts (16)16 11.94 2.85 8.29 10.24

4 Textile p roduc t s (17)17 16.41 17.80 37 .39 19.30

5 Wearing appare l (18)18 14.89 21.24 2.52 14.15

6 Tanning & leather p roduc ts (19)19 1.25 1.05 0 .63 1.14

7 Wood & wood produc ts (20)20 23 .18 16.25 9.62 20.50

8 Paper & paper products (21)21 0.11 0.18 1.44 0.29

9 Agro 81.04 67.66 38 .65 69.39

10 Non-agro 18.96 32.34 61.35 30.61

All 100.00 100.00 100.00 100.00

The fixed asset of rural agro-processing industry is Rs. 22,991 crores. As expected, substantial chunk of it, i.e. Rs. 16,002 crores is in rural OAME section (see table 2.15a).

26

Let us look at distribution of fixed assets across size for 2 digit level industry group classification. The share of OAME in fixed asset is more than four-fifths in three industry groups viz., manufacture of tobacco products, wearing apparel and leather processing. The share of DME in fixed asset is more than half in cotton ginning, etc. and more than three-fourths in paper products. Both these industry groups have insignificant presence in rural agro-processing. Their shares in the agro-processing sector fixed assets are less than 1 per cent. However, the industry group which is the largest in fixed asset chEiracteristic is food products which accounts for 44 per cent of all the agro-processing. In this industry group, establishments (NDME & DME combined) share is one-third. Next in importance in terms of fixed assets are two broad textile sectors — textile products and wearing apparels accounting for more than three-fifths of agro-processing sector's fixed assets . The fixed asset of DME is most concentrated — only two industry groups namely food processing and textiles account for more than three-fourths of all the DME agro-processing sector's fixed assets.

The industry group which is the largest in fixed asset characteristic is food products that accounts for 45 per cent of agro-processing sector's fixed assets. Next in importance in terms of fixed assets are two broad textile sectors — textile products and wearing apparels — together accounting for more than one-third of agro-processing sector's fixed assets. The fixed asset of DME is most concentrated in only two industry groups namely food processing and textiles that together account for more than three-fourths of all the DME agro-processing sector's fixed assets.

The share of two industry groups, namely tobacco products and wood products, in fixed assets is much lower than their share in value added and even m u c h lower t h a n thei r s h a r e in agro-processing employment (Table 2.15b). It is true for all size groups OAME, NDME and DME in tobacco process ing . So, tobacco processing is one of the most labour-intensive manufacturing groups in agro-processing with much less amount of fixed assets. However, it is also true of wood processing for OAME and NDME size groups. For DME, wood processing is capital intensive as reflected by the low employment share in agro-processing and significant share in value added as well as in fixed assets.

27

Table 2.15a : Fixed Assets (in Rs. Crores)

Sr. No. Industry (NIC 1998 code) OAME NOME DME All

1 Cotton ginning etc. (0145)01405 7 2 11 20

2 Food products (15)15 6,780 2,089 1,401 10.270

3 Tobacco products (16)16 1,064 56 161 1,280

4 Textile products (17)17 2,681 507 1,043 4,231

5 Wearing apparel (18)18 3,270 705 72 4,046

6 Tanning & leather products (19) 19 159 26 11 196

7 Wood & wood products (20)20 2,025 471 320 2,816

8 Paper & paper products (21)21 17 16 98 131

9 Agro 16,002 3,872 3,116 22,991

10 Non-agro 3,498 1,676 4,800 9,974

All 19,500 5,549 7,916 32,965

Table 2.15b : Distribution of Fixed Assets Across Industry groups Sr.No. Industry (NIC 1998 code) OAME NOME DME All


2 Food products (15)15 42.37 53.95 44.95 44.67


4 Textile products (17)17 16.75 13.09 33.47 18.40

5 Wearing apparel (18)18 20.43 18.20 2.30 17.60

6 Tanning & leather products (19) 19 0,99 0.68 0.35 0.85



9 Agro 82.06 69.79 39.36 69.74

10 Non-agro 17.94 30.21 60.64 30.26

All 100.00 100.00 100.00 100.00

Table 2.15c : Fixed Assets Per Enterprise

NIC98 OAME NOME DME All

0 1 4 0 5 39 ,287 74 ,974 1,715,750 96.231

15 32 ,220 109,604 212 ,852 43 ,503

16 6.582 43 ,998 82 .015 7,767

17 17,273 55 ,001 230 ,446 25 ,042

18 21 ,156 58 ,669 131,369 24 ,212

19 18,127 83 ,147 158,889 21 ,429

20 8,429 83 ,852 409 ,180 11,419

21 6 ,645 404 ,125 1,634,481 49 ,986

Agro 17,142 81 ,422 214 ,484 23 ,093

Non-agro 20 ,298 108,927 472 ,568 50 ,403

All 17,634 88 ,146 320 .684 27 ,621

28

Size Distribution of Agro-processing Sector

In number of enterprises, the share of OAME in agro-processing is more than nine-tenth. In the non agro-processing, it is little less than nine-tenths (see table 2.16a). The corresponding share of both NDME and DME in agro-processing is less than 5 per cent. In the case of non agro-processing, the share of both NDME and DME is more than 5 per cent. At two digit level, within agro-processing, the share of NDME in the number of enterprises characteristic is more than 5 per cent in case of food products , textile products and wearing apparels . In case of DME, none of the two-digit agro-processing industry group has more than 5 per cent share in the number of enterprise characteristic.

Table 2.16a : Distribution of Manufacturing Enterprises Across Size (Rural)

Sr.No. Indust ry (NIC1998 code) OAME NDME DME ALL


2 Food produc ts (15)15 89.14 8.07 2.79 100.00

3 Tobacco produc ts (16)16 98 .04 0.77 1.19 100.00

4 Textile p roduc t s (17)17 91.87 5.46 2.68 100.00

5 Wearing appare l (18)18 92.49 7.19 0 .33 100.00

6 Tanning & leather products (19) 19 95.79 3.47 0.74 100.00

7 Wood & wood products (20)20 97 .40 2.28 0.32 100.00


9 Agro 93.76 4 .78 1.46 100.00

10 Non-agro 87.09 7.78 5.13 100.00

All 92 .66 5.27 2 .07 100.00

As regards the emplojrment characteristic, the dominance of OAME size group is much less (Table 2.16b). In emplojnnent characteristic, in agro-processing industry, NDME and DME size groups have more than 7 per cent share each. At two digit level, in the case of food products, both NDME and DME has more than one-tenth share. Further, in the textile products and in the wearing apparel the share of employment of DME and NDME respect ive ly in the agro processing sector is more than 10 per cent respectively. However, in non-agro processing sector NDME £ind DME have at least one-tenth share in employment.

29

Table 2.16b : Distribution of Manufacturing Employment (Rural) Across Size

Sr.No. Indust ry (NIC 1998 code) GAME NDME DME ALL

1 Cotton ginning etc. (0145)01405 52.01 12.26 35 .73 100.00

2 Food produc ts (15)15 77.67 10.92 11.42 100.00


4 Textile p roduc ts (17)17 79.70 8.30 12.00 100.00

5 Wearing appare l (18)18 84.43 13.84 1.73 100.00




9 Agro 84.90 7.67 7 .43 100.00

10 Non-agro 61.56 9.47 28.97 100.00

All 79.83 8.06 12.11 100.00

In gross value added characteristic, dominance of OAME size group is even weaker (see table 2.16c). In agro-processing little more than three-fifths of value added is generated in OAME whereas in both NDME and DME, the share is at least one-twelfth each. In non-agro processing, DME dominates even over OAME in gross value added characteristic. In number of enterprises and employment, OAME were way ahead of DME in agro-processing and in all large Industry groups. But, in gross value added in at least in paper and paper products, 63 per cent of all value added is generated by DME. Even in the large industry group like textile products DME contributed nearly one-fourth of all value added generated in it whereas in terms of employment creation the share of DME in textile products is hardly 12 per cent. Further, in wearing apparel the share of NDME is one-fifth.

Table 2.16c : Distribution of Annual Gross Value Added Across Size Class

Sr. No. Indus t ry (NIC 1998 code) OAME NDME DME ALL


2 Food produc ts (15)15 69.06 15.96 14.98 100.00


4 Textile p roduc ts (17)17 62.61 12.45 24.94 100.00

5 Wearing apparel (18)18 77.46 20.25 2.29 100.00

6 Tann ing & leather products (19) 19 80.46 12.41 7.13 100.00



9 Agro 73.63 13.50 12.87 100.00

10 Non-agro 39.06 14.63 46.31 100.00

All 63.05 13.84 23.11 100.00

30

A similar scenario can be observed in the case of fixed assets as well. The share of OAME in fixed asset is more than four-fifths in three industry groups viz., manufacture of tobacco products, wearing apparel and leather processing (Table 2.16d). The share of DME in fixed asset is more than half in cotton ginning, etc. and more than three-fourths in paper products. Both these industry groups have insignificant presence in rural agro-processing. Their shares in the agro-process ing sector fixed a s se t s are less t h a n 1 per cent . However, the industry group which is the largest in fixed asset characteristic is food products which accounts for 45 per cent of all the agro-processing. In this industry group, establishments (NDME & DME combined) share is one-third.

The major difference is that in the case of food products, the share of NDME is one-fifth in the fixed asset characteristic whereas a similar share of NDME is observed only in the wearing apparel in the value added characteristic.

Table 2.16d : Distribution of Fixed Assets Across Size Groups

Sr. No. Indus t ry (NIC 1998 code) OAME NDME DME All


2 Food produc ts (15)15 66.02 20.34 13.64 100.00


4 Textile p roduc ts (17)17 63.37 11.98 24 .65 100.00

5 Wearing appare l (18)18 80.81 17.41 1.77 100.00




9 Agro 69.60 16.84 13.55 100.00

10 Non-agro 35.07 16.81 48 .13 100.00

All 59.15 16.83 24.01 100.00

31

CHAPTER III

FORWARD AND BACKWARD LINKAGE OF AGRO-INDUSTRY

Introduction

Production actlArity of each sector demands input supplied from a large number of production activities giving rise to interdependence of production activities. A sector is linked with other sectors which supply inputs to it and also with those which use its output as their own inputs. Any increase in production in a sector induces a larger demand for inputs from its input supplying sectors and also enables it to provide larger input supply to other producing sectors. The former type of inter-linkages is called backward linkage and the latter, a forward linkage. The most commonly used methods to measure of production linkages are based on Leontief s input-output analysis . The elements of input -output coefficient matr ix show directly how these two sectors are related in input usage.

Direct backward linkage is defined as the amount of input needed to produce a unit of output of a given sector. Uj = 2^j/X,j = Za j where a,j = X,j/X,j and Uj is the backward linkage (Venkatramaiah, 1986). But direct linkage is based on the first layer of inter-sectoral relationships. These inputs, in turn, require further inputs, and the process continues. The elements of Leontief s inverse capture the sum total effects of infinite layers of production process. Thus the direct and indirect backward linkage is defined as TUj = S Ay* where Ay* are the elements of (I-A) ^ A,j* gives direct and indirect backward linkage of sector j with sector 1. Similarly, one can define direct forward linkage as the Eimount of its output required to produce a unit of output of all other producing sector. Vj = 2jX,j/X, = 2b,j where b|, = X /Xj and Vj is the forward linkage. Similar to backward linkage, one can also estimate direct £ind indirect forward linkage on the basis of Leontief s inverse. As the purpose of this study is to estimate forward and backward linkage of the village level agro-industries and input-output matrix of rural areas is not available one can at best attempt to calculate direct backward and forward linkage of the village level agro-industries and compsire with it that of Eill-lndla.

In this chapter, our task to estimate forward and backward linkages of the village level agro-industries and compare them with all-India

33

estimates. But, agro-industry by definition is based on raw material supplied from the broad agricultural sector, and a negligible part of agro-industry's output is used as input in agricultural production as well as production of other sectors of the economy. Therefore, backward linkage of agro-Industries is relatively stronger. The strength of backward production linkage of agro-industry can work as growth impulse in the agricultural sector. Therefore, before estimating linkages of agro-industiy we will examine the agriculture X agro-industiy sub-matrix over the years to get a broad idea as to how much of agricultural output is actually processed In agro-industry, and the changes therein. Further, we will try to classify agro-industry on the basis of its input use patterns.

Plow of Agricultural Output for Intermediate and Agro-Industry Use

This is examined in two steps: i) The proportion of agricultural output which is intermediately used; and ii) The proportion of this intermediate use that actually goes for agro-processing. For this purpose, 21 agricultural sectors x 21 agro-industry sectors sub-matrix of intersectoral transaction matrix of India for the years 1983-84 and 1993-94 are used. This submatrix is obtained from detailed 115x115 sector commodity (C) x industry (1) inter-sectoral transaction matrix of all-India. The proportions of agricultural output going to in termedia te use and further to agro-processing are presented in table 3.1.

In the category of plantation crops, there are three major sectors: tea, coffee and rubber. Only 19 to 28 per cent of coffee output is available for intermediate use and the whole of intermediate use goes for processing in the agro-industry. Whereas almost the whole output of tea and rubber is processed by the agro-industry. In the category of commercial crops like jute, cotton and tobacco, almost the whole amoun t of ou tpu t available for in termediate use is processed by the agro-industry. In jute, almost two-thirds of the output goes for processing whereas in cotton crop, more than nine-tenths of output is utilised for intermediate use. But in tobacco, the percentage of intermediate use rose significantly from 71 per cent to 91 per cent in between 1983-84 and 1993-94. In sugarcane and groundnut, out of total intermediate use nearly nine-tenths goes for agro-process ing, b u t in sugarcane only half of the ou tpu t is intermediately used as opposed to 78 to 91 per cent in the case of groundnut. In groundnut crop, intermediate use has significantly fallen by 13 per cent in the five year period between 1983-84 and 1993-94.

34

In the food crop category, the proportion of output available for intermediate use and the proportion of intermediate use going for agro-process ing are generally m u c h lower t h a n t ha t of o ther categories like plantation crop, commercial crop and agricultural allied sector. Therefore, the impact of changes in agricultural output on agro-processing for these agricultural commodities is likely to be far less through raw material supply linkage. However, in cereals (paddy, wheat, jowar, bajra, maize) and in gram, al though the proportion of output used for intermediate use and the proportion of intermediate use that goes for agro-processing is still quite low yet it shows improvements in between 1983-84 and 1993-94. Only in o ther c rops sec tor , the pe rcen tage of o u t p u t avai lable for intermediate use is 36 per cent and only one-sixth of the amount of output available for intermediate use is actually processed further by ag ro - indus t ry . But p ropor t ion of o ther c rops avai lable for intermediate use has fallen substantially from 49 per cent to 36 per cent in between 1983-84 and 1993-94 and the proport ion of intermediate use that goes into agro processing has also fallen by 5 per cent from 20 to 15 per cent in the same period.

For agricultural allied category, in milk and milk products sector only 12 to 13 per cent of output is available for intermediate use but a high percentage of 72 to 75 goes for further processing in the agro-industry. In forestry and logging around half of outpiit was available for intermediate use, out of which around 60 per cent was used by the agro-industry in 1983-84. But in 1993-94, only 37 per cent is available for intermediate use but proportion used in agro-processing has gone up from 61 to 77 per cent. In the fishing sector, substantial improvements have been observed. The amount of output available for intermediate use rose from 14 per cent to 37 per cent in between 1983-84 and 1993-94 and the proportion of intermediate use going into agro-processing increased substantially from 48 per cent to 77 per cent in between 1983-84 and 1993-94.

Input Source-Based Classification of Agro-Industry

In the earlier section, the proportion of agricultural output which goes for processing was presented. There is another dimension which may weaken the growth linkages between agriculture and agro-industry. It is the degree to which agro-processing takes place. It is quite possible tha t some agro-industry sectors might under take secondary processing of already primary processed agricultural commodities whereas the other agro-industry sectors might confine themselves mainly to primary processing of agricultural products. In

35

the case of the former category of agro-industries, they would be relatively less affected by the changes in the output of agricultural commodities. Similarly, the capacity of this type of agro-industries to s t imula te growth impulse in the agr icul tura l sector also gets diminished. Let us examine the presence of such a phenomenon on the bas i s of the all-India in ter-sectoral t r ansac t ion matr ix of 1983-84.

Table 3.1 : Proportion of Agricultural Output Going to linteimediate Use and Further to Agro-Processing

Sr. No.

Commodity IP-OP Transac t ion Code

Intermediate Use Agro - Processing Sr. No.

Commodity IP-OP Transac t ion Code 1983-84 1993-94 1983-84 1993-94

Plantat ion Crop

1 TEA 1.00 0.87 1.00 1.00 2 COFFEE 0.28 0.19 0.99 0.99 3 RUBBER 1.20 1.07 0.99 0.99 4 COCONUT

Commercial Crop

0.12 0.12 0.90 0.78

5 SUGARCANE 0.57 0.48 0.87 0.86 6 GROUNDNUT 0.91 0 .78 0.90 0.86 7 JUTE 1.00 0.66 1.00 0.95 8 COTTON 1.01 0.91 0.99 0.97 9 TOBACCO

Food Crop

0.71 0 .91 0.99 0 .98

10 PADDY 0.16 0.22 0.04 0.06 11 WHEAT 0.23 0 .23 0.13 0 .23 12 JOWAR 0.05 0.07 0.01 0.05 13 BAJRA 0.08 0.14 0.01 0.06 14 MAIZE 0.14 0.17 0.36 0.38 15 GRAM 0.35 0.40 0.04 0.07 16 PULSES 0.22 0.18 0.06 0.05 17 OTHER CROPS

Agricultural Allied Sector

0.49 0.36 0.20 0.15

18 MILK & MILK PRODUCTS 0.12 0 .13 0.72 0.75 19 OTHERLIVESTOCKPRODUCTS 0.33 0.22 0.36 0.34 20 FORESTRY & LOGGING 0.45 0.37 0.61 0.77 2 1 FISHING 0.14 0.37 0 .48 0.77

NOTE : Proportion showing more than unity reflects the significance of imports.

SOURCE :1 . Govt, of India. Input-Output Matrix, 1983-84 (Commodity into Industry), Absorption Matrix, 115 x 115 Sector. New Delhi: Dept. of Statistics, Ministry of Planning (available only in floppies), September 1990.

2. Govt, of India. Input-Output Matrix, 1993-94 (Commodity into Industry), Absorption Matrix, 115 x 115 Sector. New Delhi: Dept. of Statistics, Ministry of Planning (available only in floppies), September 1998.

36

The availability of specific raw materials is important for the growth of agro-industr ies in cases where an agro-industry buys some significant amount of that raw material for its own production. Sources of main raw material input of an agro-industry could either be from an agricultural sector or from an agro-industry sector. The magnitude of agro-industry's transaction can be traced from the agricultural-agro-industry inter-sectoral transaction matrix. Input coefficient (proportion of input to total output) having values 0.05 has been taken to be the cut-off point in classifying agro-industries into different categories. On the bas i s of th is criterion, agro-industries can be put into four categories:

(i) a,j > = 0.05 for one or more agricultural sector i and a <0.05 for all agro-industry sector i.

(ii) a,j > =0.05 for one or more agro-industry sector i and a,j <0.05 for each of the agricultural sectors.

(iii) ay > = 0.05 for at least one agricultural sector i as well as one agro-industry sector and agricultural sector is the largest input supplier.

(iv) a,j > = 0.05 for at least one agro-industry sector i as well as one agricultural sector and agro-industry is the largest input supplier.

They are named as (i) mainly agricultural input purchasing industry; (ii) mainly agro-industry input purchasing industry; (iii) primarily agricultural input and secondarily agro-industry input purchasing industry and (iv) primarily agro-industry and secondarily agricultural input purchasing industry.

Only beverage (code 39) is the agro-industry which does not even buy 5 per cent of input from at least one agricultural or agro-tndustiy and therefore cannot be categorised. Table 3.2 presents the overall picture. Columns 4 and 6 provides the input coefficients of main inpu t supplying agr icu l tura l and agro- indus t ry sec tors respectively with the main input supplying industry code in the bracket. Columns 5 and 7 provide the share of agricultural and agro-industry inputs to total material inpu t s of agro-industry. Column 3 is total mater ia l inpu t sha re to o u t p u t (the direct backward production linkage).

On the basis of a similar table generated from all-India inter-sectoral t r a n s a c t i o n mat r ix of 1993-94, one can observe s u b s t a n t i a l

37

reshuffling of agro-industries across different categories in terms of input use patterns. The changes have largely affected category iii and iv (see Table 3.3).

Table 3.2 : Classification of Agro-Industries on the Basis of Main Input Source in 1983-84

INDUS DESCRIPTION OF THE CODE MATE AGRI AGRI- AGRO- AGRO-TRIAL RIAL CULTU CULTU- INDUS INDUSCODE INTEN RAL IN- TAL IN TRY IN TRY IN

SITY PUT PUT TO PUT PUT TO (MI/OP) SOURCE TOTAL

MATERIAL

INPUT

SOURCE TOTAL MATERIAL

INPUT

(1) (2) (3) [• (4) (5) (6) (7)

(i) MAINLY AGRICULTURAL INPUT

3 3 SUGAR 0 .8020 0 .5626 (8) 0 .7057 0 .0218 3 4 KHANDSARI.BOORA 0.7259 0 .4496 (8) 0 .6267 0 .0736 3 6 EDIBLE OIL OTHER THAN VANASPATI 0 .9600 0 .4551 (9)

0 .3299 (17) 0 .8390 0 .0367

3 8 MISCELLANEOUS FOOD PRODUCTS 0.8197 0 .2078 (18) 0 .5249 0 .2258 51 OTHER WOOD AND WOOD PRODUCTS 0.6928 0 .4975 (21) 0 .7189 0 .0698

(ii) MAINLY AGRO-INDUSTRY INPUT

3 5 HYDROGENATED OIL 0.8608 0 .0410 0 .4318 (36) 0 .5247 41 KHADI.COTTON TEXTILE IN HANDLOOM 0.3914 0 .0049 0 .2163 (42) 0 .6839 4 7 CARPET WEAVING 0 .3614 0 .0242 0 .0710 (42)

0 .0532 (43) 0 .5301

4 8 READY MADE GARMENTS 0 .3673 0 .0020 0 .1139(42) 0 .5677 4 9 MISCELLANEOUS TEXTILE PRODUCT 0.5871 0 .0236 0 .1278 (42)

0 .1070(49) 0 .5559

52 PAPER,PAPER PRODUCTS & NEWSPAPER 0.7045 0 .0695 0 .2454 (52) 0 .3739 54 LEATHER FOOTWEAR 0.5118 0 .0369 0 .2158 (55) 0 .6360

(iii) PRIMARILY AGRICULTURAL INPUT AND SI :CONDARILY AGRO-INDUSTF lY INPUT

3 7 TEA AND COFFEE PROCESSING 0.7786 0.3555 (12) 0 .4902 0 .1720(37) 0 .2550 4 0 TOBACCO PRODUCTS 0.5412 0 .1443(16) 0 .3571 0 .0897 (40) 0 .2342 4 2 COTTON TEXTILES 0.6888 0 .2066(11) 0 .3016 0 .1497 (42) 0 .2469 4 6 JUTE.HEMP.MESTA TEXTILES 0 .6750 0 .2964 (10) 0 .4457 0 .1004 (46) 0 .1858 55 OTHER LEATHER & LEATHER PRODUCTS 0.7625 0 .2375 (20) 0 .3435 0 .1666(55) 0 .2566

(iv) PRIMARILY AGRO-INDUSTRY INPUT AND £ ECONDARILY AGRICULTURE iL INPUT

4 3 WOOLLEN TEXTILE 0.6631 0 .0874 (20) 0 .1457 0 .1992 (43) 0 .3326 4 4 SILK TEXTILES 0.5891 0 .1619(20) 0 .2765 0 .2463 (44) 0 .4945 5 0 FURNITURE & FIXTURES 0 .4233 0.0954 (21) 0 .2270 0.1071 (51)

0 .0641 (50) 0 .4306

(V) UNCLASSIFIED

3 9 BEVERAGES 0 .5873 0 .1069 0 .2015

NOTE: 1. 2.

SOURCE

Figures in bracket are input industry source code. For a description of agricultural input source code see table 3.1.

Govt, of India. Input-Output Matrix. 1983-84 (Commodity into Industry), Absorption Matrix. 115x 115 Sector. New Delhi: Dept. of Statistics, Ministry of Plaiming (available only in floppies), September 1990.

38

Table 3.3 : Classification of Agro-Industries on the Basis of Main Input Source in 1993-94

INDUS DESCRIPTION OF THE CODE MATE AGRI AGRI- AGRO- AGRO-TRIAL RIAL CULTU CULTU- INDUS INDUSCODE INTEN RAL IN- TAL IN TRY IN TRY IN

SITY PUT PUT TO PUT PUT TO (MI/OP) SOURCE TOTAL

MATERIAL

INPUT

SOURCE TOTAL MATE

RIAL INPUT

(1) (2) (3) (4) (5) (6) (7)

(i) MAINLY AGRICULTURAL INPUT

3 3 SUGAR 0 .7778 0 .5309 (8) 0 .6867 0 .0279 34 KHANDSARI,BOORA 0.7971 0 .2842 (8)

0 .2350 (17) 0 .6526 0 .0635

3 6 EDIBLE OIL OTHER THAN VANASPATI 0 .7768 0 .5427 (9) 0 .0851 (17)

0 .8289 0 .0229

3 8 MISCELLANEOUS FOOD PRODUCTS 0 .8179 0 .1914(18) 0 .0873 (17)

0 .5846 0 .1279

4 0 TOBACCO PRODUCTS 0 . 5 8 4 8 0 .1084(16) 0 .0685 (21)

0 .3227 0 .1627

4 6 JUTE,HEMP,MESTA TEXTILES 0 .6800 0.1817 (10) 0 .2680 0 .0663 51 OTHER WOOD AND WOOD PRODUCTS 0 .5098 0 .2346 (21) 0 .4682 0 .1373

(ii) MAINLY AGRO-INDUSTRY INPUT

4 1 K H A D i . c a r r o N T E X T I L E I N H A N D L O O M 0.4722 0 .0381 0 .2191 (42) 0 .5453 4 7 CARPET WEAVING 0.4222 0 .0046 0 .1396(43) 0 .4795 4 8 READY MADE GARMENTS 0 .6512 0 .0007 0 .2263 (42) 0 .6429 49 MISCELLANEOUS TEXTILE PRODUCT 0 .6602 0 .0342 0 .1472(42)

0 .0548 (15) 0 .4649

52 PAPER,PAPER PRODUCTS & NEWSPAPER 0 .7383 0 .0804 0 .2057 (52) 0 .3254

(iii) PRIIWARILY AGRICULTURAL INPUT AND SE ;CONDARILY AGRO-INDUSTF lY INPUT

3 5 H Y D R O G E N A T E D OIL 0.8819 0 .3024 (17) 0 .4534 0 .1020(36) 0 .1370 3 7 TEA AND COFFEE PROCESSING 0 .7542 0 .2657 (12) 0 .3909 0 .0750 (37) 0 . 1 1 0 0 42 COTTON TEXTILES 0 .7488 0.2637 (11) 0 .3552 0 .0748 (42) 0 .1088 44 SILK TEXTILES 0 .5700 0 .1259 (20) 0 .2273 0 .1009(44) 0 .2855 50 FURNITURE & FIXTURES 0.4652 0 .1280(21) 0 .2787 0 .0580 (51) 0 .2452

(iv) PRIMARILY AGRO-INDUSTRY INPUT AND S ECONDARILY AGRlCULTUR/> iL INPUT

4 3 WOOLLEN TEXTILE 0 .7202 0 .0883 (20) 0 .1235 0 .1278(42) 0 .3952 54 LEATHER FOOTWEAR 0 .6242 0 .0528 (20) 0 .0944 0 .1440 (55) 0 .3169 5 5 OTHER LEATHER & LEATHER PRODUCTS 0 .7261 0 .1022(20) 0 . 1 4 6 5 0 .2788 (55) 0 .4009

(v) UNCLASSIFIED

39 BEVERAGES 0 .5554 0 .1121 0 .2232

NOTE : Figures in bracket are input industry source code.

SOURCE : Govt, of India. Input-Output Matrix, 1983-84 (Commodity into Industry), Absorption Matrix, 115x 115 Sector. New Delhi: Dept. of Statistics, Ministry of Planning (available only in floppies), September 1999.

39

Tobacco products (code 40) and jute products (code 46) have shifted from primarily agricultural input based (category iii) to mainly agricultural input based (category i). Leather products (code 55) has shifted from category iii to category iv (mainly agro-industry input based). In contrast, two industries viz., silk textiles (code 44) and furniture & fixtures (code 50) have shifted from category iv to category iii. The agro-industries belonging to category i (mainly agricultural input based) have remained intact. From category ii (mainly agro-industry based), hydrogenated oil (code 35) and leather footwear (code 54) have shifted to category iii and iv respectively. The net result is that in the span of ten years from 1983-84 to 1993-94, the number of agro-industries belonging to category ii has fallen by one each and net addition of two agro-industries has occurred in category i. It shows that the agro-industry has become more of a primary processing type over the years.

Backward Linkage of Village Level Agro-industries

The backward linkage of agro-industry shows the amount of input that the village level agro-Industry requires producing one unit of output. The detailed raw material used in 56* round (2000-01) is not available in the unit level data of that round. So we have to calculate backward production linkage on the basis of 51*' round (1994-95) data. The direct backward production linkage of agro-industry is 0.5813 (see Table 3.4). It is much higher than that of the non-agro industry figure of 0.4886. In other words, it shows that the share of input in output in agro-industry is 58.13 per cent as opposed to 48.86 per cent in the case of non-agro industry. Now let us look at the backward production linkage of agro-industries at the two digit level. Column 8 of Table 3.4 shows that input coefficient as backward production linkage of food products is higher at 0.7240. In contrast, In the case of wood & wood products, it is less than half of food products level at 0.3563. Apart from food products, only in case wool textiles and leather & leather products backward production linkage is higher than that of overall agro-industries. In all o ther agro- indus t r ies at the two digit level, the backward production linkage is less than the agro-industry level.

Another important aspect is that, not only the share of input in agro-industry is higher, the share of raw material in total input is also much higher — more than four-fifths. But in the non-agro industry, it is much smaller—only little more than three-fifths. At the two digit level, the share of raw material in total input is more than four-fifths in five out of nine agro-industries at this level of

40

disaggregation. Only in the case of paper products it is much less — at less than two-fifths.

Now let u s see the use pattern of agricultural and agro-industry inputs in agro-industry (Table 3.4). More than half of all agro-industries' input comes from agriculture and one-fifth of all inputs originates from the agro-industry itself. But as we analyse on the basis of the two-digit level of disaggregation, we can divide the different agro-industry groups into two broad categories - one category of agro-industry that largely uses agricultural produce as the main input and the other that uses agro-industry products as the main input. The industry group of food products, beverages & tobacco products and wood & wood products are largely based on agricultural products, i.e. are primary processing agro-industries. The backward product ion linkage of food products with respect to agricultural sector is as high as 0.50. However, in beverages & tobacco products and in wood products, the backward linkage with respect to agricultural sector is around 0.20. On the other hand, cotton textiles, wool textiles, textile products, paper products and leather products are largely based on agro-industry products, i.e. these are the secondary processing agro-industries. The backward linkage of these agro-industries with respect to agro-industry sector is much higher than the backward linkage with respect to the agricultural sector. The backward linkage of these industries with respect to the agro-industry sector is at least around 0.20. Only in the case of leather processing, the backward linkage with respect to agro-industry is very high at 0.41.

There is some element of underestimation in the calculation of these backward linkages with respect to certain sectors. The village level agro-industries backward linkages are calculated on the basis of various input uses classified by commodity classification on the basis of unit level data. For each enterprise, input commodities are given by specified commodities for main two or three main items and the rest of the items are classified under miscellaneous categories. Therefore, input commodities are classified into three categories — agr i cu l tu ra l p r o d u c t s , ag ro - indus t ry p r o d u c t s and non-ag ro manufactured products. Apart from them, there are miscellaneous products and products that are not specified. At agro-industry level, the share of miscellaneous products in the total input is only 4.62 per cent, and so it does not create much problem. But in the case of two industry groups viz.. wool textiles and jute textiles, the shares of miscellaneous commodities in total input is as high as 27 and 41 per cent respectively. Consequently, it has created major problem for

41

j u t e textiles. Although the backward production linkage of ju te textiles Is as high as 0.463, yet its backward linkages with respect to the agro-Industries and agriculture are as low as 0.123 and 0.074 respectively. But given the limitation we can classify it as largely the secondary processing industry at village level.

Comparison of Backward Production Linkage at Village Level with that of All-India

Let u s now compare the backward production linkage of the agro-industry at the all-India level with that of the village level agro-industries. For the all-India calculations we have used the all-India inter-sectoral transaction matrix of 1993-94 as compared to that of the 1994-95 unit level data for the village level Industries. The backward production linkage of the agro-industry at the all-India level is much higher at 0.708 as compared to 0.581 in the case of village level. But the classification of two-digit level agro-industry at all-India as primary and secondary processing is similar to the village level agro- indus t r ies . Similar to the village level, food products , beverages & tobacco and wood products are primary processing at the all-India level as well (see Table 3.5). But in the case of cotton textiles and jute textiles at the all-India level it is largely primary processing agro-industry ra ther than secondary processing as reflected by their much larger backward production linkages with respect to agriculture than those with respect to agro-industry.

It goes to show tha t for these two industry groups, the primary processing is done outside the village agro-industry level and the village level ag ro - indus t r i e s in these two g roups a re largely processing the agro-industry product produced outside rural areas. But agro-industries which are secondary processing at adl-lndia level are also secondary processing at the village level. These industries are wool textiles, textile products and paper products and leather products.

In three agro-industiy groups (food products, beverages & tobacco and wood products) tha t are engaged in primary processing — process ing largely agr icul tura l raw mater ia l s — the sha re of agricultural raw material in total raw materials in the village level agro-Industries are higher than that of the overall economy level. Similarly, in t he case of ag ro - indus t r i e s t h a t are secondary processing, using agro-industry inputs as main raw materials, say for paper products and leather products, the share of agro-industry

42

input to total raw materials at the village level is much higher than that of the all-India. Only in the case of wool textiles, the input use patterns are not very dissimilar. However, in textile products, at all-India level, the share of agro-industry input to total raw materials is much higher that that of the village level.

It goes to show that as compared to the all-India agro-industiy, the village level agro-industry units are generally lying at two extremes — either at the beginning of primary processing or at the fag end of secondary processing.

Forward Linkage at Village Level

The forward linkage captures the proportion of agro-industry output that goes as input into other sectors of the village economy. Now let us look at the proportion of output of agro-industry that goes as input of other sectors. Since, ours is a manufacturing enterprise survey, we can only see the utilisation of agro-industry output as input to other manufacturing industries. The proportion of output that gets used as input is only 0.122. But almost all of it, that is, 0.119 is used up in agro-industry itself. The rest goes to the non-agro industry sector. Similar is the story of the agro-industry groups even when observed at the two digit level. At the two digit level, output of cotton textiles and wool textiles industries get substantially used in the output of textile products — to the extent of 14 and 11 per cent of the output of these industries respectively. However, the use of agro-industry output as input of the non-agro industries is negligible—not even 1 per cent of agro-industry output is used as input. This is the case even in any two-digit agro-industry level except for paper and paper products. In paper & paper products, 3 and 2 per cent of all output is used in basic chemicals and other manufacturing sectors possibly as packaging materials.

However, the intra-industry use is relatively substantial. At the agro-industry level, 11 per cent of all the agro-industry output is used as intra-industry use. A simiilar phenomenon can be seen at two-digit level as well. One-fourth of all output of cotton textiles and wool textiles is used as input within the industry groups themselves. Strongest emid almost fully intra-industry trade dominated industry group is the leather processing which uses 38 per cent of all output as input within the industry itself.

However, the fact is that an overwhelming share of the agro-industry output is used in inter-industry trade within the manufacturing

43

sector. Since the agro-industry by definition processes agricultural raw ma te r i a l s , it is not used s u b s t a n t i a l l y as i npu t in the agricultural sector. It, in effect, implies that most of the agro-industry output produced by the village level agro-industry goes into the final consumption basket.

Certain Assumptions are Inherent in this type of Analysis

1. Different kinds of leakages are involved since the rural sector is not a self contained unit. It is possible that some of the output of the village level agro-industry units is used as input in organised segment or urban unorganised segment of manufacturing.

2. Input used in village level agro-industry—^both agricultural raw materials and agro-industry processed raw materials—originate from imports. Further, manufactured inputs also could come from the o rgan ised sec to r or from u r b a n segment of unorgan i sed manufacturing.

Comparison of Forward Production Linkage at Village Level with that of AU-India

Now let us compare the direct forward production linkage of village level agro-industry with that of all-India. At the all-India level, the forward linkage of agro-industry to agro and non-agro industry is somewhat higher as compared to that of the village level industries. Similar to the village level, the bulk of output of the agro-industry is absorbed in agro-industry itself At the two digit level, similar to the phenomenon of the village level, a substant ial proportion of the output of cotton textiles and wool textiles gets used in the agro-industry itself, but unlike at the village level, the proportion of the output of wool textiles and cotton textiles that get used in the agro-industry is somewhat less. But nearly half of paper products and ju te textiles' output gets used in the agro-industry's production which is far higher than that of the village level. In contrast, in the case of leather products, a smaller proportion of its output gets used in the agro-industry compared to the village level.

However, major differences lie in the use of the agro-industry output in the non-agro industry sector. At the village level, hardly 0.1 per cent of the agro-industry output gets used in the production process of the non-agro industry. But at the all-India level, it is around 5 per cent. The major industry groups whose substant ia l part of output goes to non-agro use are jute textiles, paper products and

44

Table 3.4 : Backward Production Linkage of Agro-Industries (Village Level], 1994-95

(Values in Rs. Lakh)

or

Sr. No.

Industry Value of Input

(IP)

Value of Out

put (OP)

Value of Ag

ricultural IP

Value of Agro Industry IP

Value of Non-agro In

dustry IP

Value of

Others IP

Input Coeflicl-ent (IC) column

(2/3)

Agriculture

IC column

(4/3)

AgrolC column

(5/3)

Non-agro IC column

(6/3)

Misc. Items

to total input

Share ofRM in IP

Share of

Agri in RM

Share of

Agro inRM

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Food products 786,126 1,085,883 536,928 88,437 1,789 158,971 0.7240 0.4945 0.0814 0.0016 0.0216 82.73 82.56 13.60

2 Beverages & tobacco products

76,143 173.082 38,725 10,501 589 26,328 0,4399 0.2237 0.0607 0.0034 0.0432 70.27 72.37 19.63

3 Cotton textiles 71,866 162,778 891 43,743 815 26.417 0.4415 0.0055 0.2687 0.0050 0.0072 65.39 1.90 93.08

4 Wool textiles 72,232 123,540 5,916 35,114 109 31,093 0.5847 0.0479 0.2842 0.0009 0.2719 86.30 9.49 56.33

5 Jute textiles 7.572 16,352 1,204 2,020 0 4,348 0.4630 0.0736 0.1235 0.0000 0.4125 85.02 18.70 31.37

6 Textile products 72,548 170,889 11,436 40,612 2,674 17,826 0.4245 0.0669 0.2377 0.0156 0.0861 86.17 18.29 64.97

7 Wood & wood products

125,317 351,716 70,720 16,431 3,273 34,893 0.3563 0.2011 0.0467 0.0093 0.0487 78.32 72.06 16.74

8 Paper & paper products

11,804 21,471 10 3,980 262 7,553 0.5498 0.0005 0.1854 0.0122 0.0126 38.54 0.23 87.47

9 Leather & leather products

23,428 39,462 257 16,264 2,336 4,570 0.5937 0.0065 0.4121 0.0592 0.0650 87.52 1.25 79.33

10 Agro Industry 1.247,035 2,145,172 666,088 257,102 11,846 311,999 0.5813 0.3105 0.1199 0.0055 0.0462 80.62 66.26 25.57

11 Non-agro Industry

306,460 627,266 11,002 3,909 111,520 180,029 0.4886 0.0175 0.0062 0.1778 0.0741 63.04 5.69 2.02

Total 1,553,495 2,772,439 677,090 261,011 123,366 492,028 0.5603 0.2442 0.0941 0.0445 0.0517 77.15 56.49 21.78

Table 3.5 : Backward Production Linkage of Agro-Industries (all India Level), 1993-94

(Values In Rs. Lakh)

Sr. No.

Indus t ry Value of Input

(IP)

Value of Ou tpu t

(OP)

Value of Agricultura l IP

Value of Agro Indu

stry IP

Value of Non-agro Indus t ry

IP

Input Coefficien t (IC) column

(2/3)

Agriculture IC Column

(4/3)

Agro IC

Column (5/3)

Non-agro IC Column

(6/3)

Manufacturing

IC Column (10+11)

Sha re of Agri in RM

Share of Agro In RM

1 2 3 4 5 6 7 8 9 10 11 12 13

1 Food products 4.122,969 5,150,274 2,522,572 368,805 176,002 0.801 0.490 0.072 0.034 0.106 81.39 11.90

2 Beverages & tobacco products

710.366 1,233,988 184,562 128,393 107,384 0.576 0.150 0.104 0.087 0.191 43.29 30.12

3 Cotton textiles 2,219,778 3,117,885 726,372 327,087 186,054 0.712 0.233 0.105 0.060 0.165 58.03 26.13

4 Wool textiles 227,596 339,453 34,767 83,175 25 ,250 0.670 0.102 0.245 0.074 0.319 23.88 57.14

5 J u t e textiles 163,576 240,563 43,836 10,841 14,580 0.680 0.182 0.045 0.061 0.106 61.69 15.26

6 Textile products 948,555 1,475,490 13,764 536,039 105,862 0.643 0.009 0.363 0.072 0.435 2.09 81.52


504,578 1,007,363 218,513 79,391 56 .400 0.501 0.217 0.079 0.056 0.135 61.32 22.28


663,872 898,657 53,390 215,994 93 ,568 0.739 0.059 0.240 0.104 0.344 12.82 51.86


550,799 815,238 67,557 199,631 83,634 0.676 0.083 0.245 0.103 0.347 19.22 56.80

10 Agro Indust ry 10,112,089 14,278,911 3,865,334 1,949,355 848,733 0.708 0.271 0.137 0.059 0.196 57.05 28.77

Table 3.6 : Forward Production Linkage of Agro-Industries (Village Level, 1994-95)

4

Sr. No.

Industry Food products

Bever-ages&

tobacco products

Cotton textiles

Wool textiles

Jute textiles

Textile products

Wood & wood

products

Paper & paper

products

Leather &

leather products

Agro Non-Agro

Total

1 2 3 4 5 6 7 8 9 10 11 12 13

1 Food products 0 .08102 0 .00023 0 .00004 0 .00213 0 .00003 0 .00028 0 .00089 0.00060 0 .04123 0 .00285 0 .03254

2 Beverages & tobacco products 0 .00423 0 .03376 0 .00017 0 .00000 0 .00009 0 .00003 0 .00490 0 .00094 0 .00400

3 Cotton textiles 0 .00004 0.00001 0 .25543 0 .00499 0 .00029 0.00808 0 .00025 0 .00079 0 .00029 0 .02039 0 .00130 0 .01607

4 Wool textiles 0 .00161 0 .25016 0 .00087 0.02299 0 .00002 0 .01637 0 .00017 0 .01270

5 Jute textiles 0 .00158 0 .10651 0.00008 0.00001 0 .00094 0 .00073

6 Textile products 0 .00095 0 .13825 0 .10966 0 .01323 0 .02436 0 .00003 0 .00002 0 .01893 0 .00426 0.01561

7 Wood & wood products 0 .00005 0 .00004 0 .00018 0 .00318 0 .00369 0 .04402 0 .00799 0 .00002 0 .00766 0 .00522 0.00711

8 Paper & paper products 0 .00022 0 .00002 0 .17427 0 .00186 0 .00042 0 .00153

9 Leather & leather products 0 .00051 0 .00008 0 .00052 0 .00250 0 .00068 0.38651 0 .00758 0 .00372 0.00671

10 Agro Industry 0 .08607 0 .03500 0 .39727 0 .36703 0.12461 0.06176 0 .04469 0 .18464 0 .38747 0 .11985 0 .01889 0.09701

11 Non-agro Industry 0.00071 0 .00002 0 .00218 0 .00040 0 .00022 0.00359 0 .00243 0 .05692 0.00101 0 .00182 0 .17779 0 .04163

Total 0 .08679 0 .03502 0 .40006 0 .36745 0 .12484 0 .06562 0 .04713 0 .24156 0.39254 0 .12183 0 .19820 0 .13910

Note: The forward production linkage is presented across columns.

wood products with 23 , 16 and 27 per cent of output of these industries respectively being absorbed in non-agro industries. The obvious explanation is that output of these three agro-industries gets used in packaging of output of non-agro products widely used in branded products. This does not get captured within the village level. At the maximum, at the village level, only 5 per cent of output of paper products gets used in non-agro industries. For jute textiles and wood products, the proportion of output that is used in the non-agro industries is less than 1 per cent. The implication is that within the village economy, the output of these three industry groups does not substantially get used up in the inter-industry production.

Table 3.7 : Forward Production Linkage of Agro-Industries (AU India), 1993-94

(Values in Rs. Lakh)

Sr. No.

Industry Value of Output

Used in Agro-

industries

Used in Non-agro industries

% output used in

agro

% output used in

non-agro

1 Food products 5.074.229 344.997 19.998 0.068 0.004

2 Beverages & Tobacco products

1.175.520 43,335 781 0.037 0.001

3 Cotton textiles 2,807,051 718.993 61,091 0.256 0.022

4 Wool textiles 301.158 105,439 2,103 0.350 0.007

5 Ju te textiles 183,805 86,341 41.639 0.470 0.227

6 Textile products 1.388,471 51,998 115,386 0.037 0.083


932.545 132,056 157,405 0.142 0.169


913.263 454,537 252.580 0.498 0.277


723.677 190,328 6,149 0.263 0.008

10 Agro Industry 13,499,718 2,128,024 657.132 0.158 0.049

48

CHAPTER IV

FINANCIAL STATUS AND ACCESS TO CREDIT MARKETS

This present chapter gives a detailed account of financial status of agro enterprises. The financial status has been evaluated in terms of Gross Value Added (GVA), profit, capital output ratio, expenses and receipts of the enterprises. Further, the chapter also deals with the issue of access to credit market by different types of enterprises in general. In order to see the cross sectional variations, the analysis follows two parallel tracks: (a) agro vis-d-vis non-agro enterprises in particular, and (b) type of enterprise i.e. GAME, NDME, and DME. The following discussion is based on information pooled from the unit level data base of the 56* round of the NSSO, which relates to the year of 2000-01.

1. Gross Value Added and Profit

Gross Value Added (GVA) and profit are among the most important indicators of financial health of enterprises. Value added represents tha t par t of production, which is an actual contr ibution of an enterprise to the economy. Value added is calculated by deducting 'total operating expenses'^ from the value of 'total receipts'* during the reference period. A part of the gross value added which is left after the factor payments to land, capital and labour as rent , interest and wages respectively is counted as total profit (which is also known as factor payment to entrepreneur). The rate of profit, hence, of an enterprise depends on the amount of GVA on the one hand and the amount paid as factor pajnnents on the other. While the GVA per enterprise (per worker) shows the productivity of the enterprise (labour), the rate of profit shows an overall efficiency of the enterprise. The 56"* round NSSO provides information about the GVA by two approaches, viz. 'production' and 'income' approach. The income approach is a direct assessment of the GVA, while the production approach follows a systematic calculation of the gross

3. Operating expenses of an enterprise is the total value of raw materials , electricity, fuel, lubr ican ts and auxil iary mater ia l s consumed; cost of maintenance, services purchased and other expenses incurred during the reference period.

4. The sale value of products and byproducts together with the value of services rendered to other concerns and other receipts incidental to entrepreneurial activities are considered as total receipts.

49

value added by es t imat ing different costs and receipts of the enterprisei^

Table 4.1 gives the annual gross value added, by production and income approaches separately, and profit per worker by type of enterprise. The GVA per worker is slightly lower by income approach in comparison with that of the production approach across all the enterprises mainly because of the lower reporting of surplus value through direct income approach in comparison with the systematic calculation of surplus value through the production approach. The difference between the two approaches is the highest in the case of own account enterpr ises (GAME) for agro as well as non-agro enterprises. The difference is the lowest in the case of directory enterprises. However, it is important to note that irrespective of the size of enterprise, the GVA per worker is lower in agro enterprises t han in the non-agro enterprises by both the approaches . The difference of GVA per worker between agro and non-agro is prominent in the case of directory enterprises followed by non-directoiy and own account enterprises. In the directory enterprise category, the GVA per worker in the non-agro en te rpr i ses is approximately 50 per cent higher than that in the agro enterprises. In the case of profit per worker, it is 25 per cent higher. Profit per worker is lower in the agro than in the non-agro enterprises in all the types of enterprises.

Table 4.1 : Annual Gross Value Added and Profit Per Worker Types of enterprise GVA per Worker (Rs.) Profit per

worker (Rs.) Profit per

worker (Rs.) Production Income

OAME Agro 8,551 8,280 8.140 Non-agro 9.930 9,597 9.432 All manufac tur ing 8,783 8,501 8.357 NDME Agro 17,365 17,228 10,548 Non-agro 24 ,162 23 .690 12,811 All manufac tur ing 19,103 18,880 11.127 DME Agro 17,080 16.243 7.399 Non-agro 25 ,023 25 ,168 10,411 All manufac tur ing 21 ,210 20 .884 8.965 All enterprise Agro 9,861 9.558 8.270 Non-agro 15,651 15.443 10.036 All manufac tur ing 11,120 10.838 8.654

5. Invariably the GVA directly reported by the income approach Is less than the GVA estimated by the production approach.

50

However, it is important to note that among the NDME and DME groups, the difference of profit per worker is not that large as the difference of GVA per worker between agro and non-agro enterprises. This suggests that although the GVA and profit per worker are lower in the agro enterprises, the agro enterprises have better capacity to convert the GVA into profit in comparison with the non-agro enterprises, which arises largely because of low proport ions of payment to other factors of production. The reverse is the situation in the GAME group, thereby implying that the agro enterprises have less capacity to convert the GVA into profit. In this respect only a marg ina l difference is observed be tween agro and n o n - a g r o enterprises in the GAME category.

Table 4.2 : Per Worker GVA and Profit in Different Industry Groups within Agro Industry

Indus t ry g roups a t NIC 2 digit GAME NDME DME All

GVA per worker (Rs.)

Cotton ginning, cleaning & bailing

1 Food p roduc t s and beverages 10,929 17,968 16,133 12,291

2 Tobacco produc ts 6,596 14,692 8,729 6 ,915

3 Textiles 7,244 13,831 19,166 9,221

4 Wearing appare l 10,424 16,627 15,050 11,362

5 Tanning a n d dress ing of lea ther 14,460 26 ,322 2 4 , 6 4 3 15,809

6 Wood and wood produc ts 7,436 23 ,489 39 ,058 8,470

7 Paper and paper p roduc ts 2 ,975 25 ,686 48 ,364 8,902

Profit per worker (Rs.)

Cotton ginning, cleaning & bailing

1 Food p roduc t s a n d beverages 10,203 12,065 8.741 10,239

2 Tobacco produc ts 6 ,475 8,064 4 ,670 6,354

3 Textiles 6,929 7,581 6,282 6 ,905

4 Wearing appare l 9,837 10,037 6 ,255 9 ,803

5 Tanning a n d dress ing of leather 13,776 14,915 10,090 13.692

6 Wood and wood produc ts 7,170 12,545 12,952 7 ,453

7 Paper a n d paper p roduc ts 2 ,857 6,239 13,136 4 ,148

The lower value of GVA per worker in the agro enterprises is a reflection of lower worker productivity in these en terpr i ses in comparison with that in the non-agro enterprises. The reason may be lower levels of infusion of technology in the agro enterprises in comparison with that in the non-agro enterprises. Within the agro enterprise the industry groups with higher infusion of technology

51

report the higher productivity. In general. Tanning and dressing of leather & leather products' shows highest value of both GVA and profit per worker. However, among the directory establishments 'Paper and paper products' followed by 'Wood and wood products' shows the highest productivity per worker both in terms of GVA and profit (Table 4.2).

We have seen in Chapter II that within the DME categoiy 'Paper and paper products' has the highest value of fixed asset per enterprise. Similarly, within the DME, 'Wood and wood products' also shows higher productivity in comparison to 'Tanning and dressing of leather'. Within the GAME group 'Food products and beverages' and "Wearing apparel' occupy the second and third position in terms of GVA and Profit per worker. Overall the second highest productivity is recorded in 'Food products and beverages' followed by 'Wearing apparel' and Textiles' (Table 4.2).

Further, the productivity in the two industry groups viz. "Wood and wood products' and 'Paper and paper products' is much higher in the DME group in comparison with those among the NDME and GAME groups. This again may be linked with higher value of fixed asset per enterprise in these two industry groups in comparison with that in the own account group.

In terms of productivity per worker within the agro enterprises, the non directory enterprises (NDME) turn out to be the most efficient as both the GVA and profit per workers in NDME agro enterprises is the highest. In contrast to this the GVA per worker Is the highest in DME in the case of non-agro enterprises and profit per worker is the highest in NDME both for agro and non-agro enterprises. However, it h a s to be r emembered t h a t the agro Indus t r i e s a re mainly concentrated in the GAME categoiy where productivity is the lowest. Although GVA and profit per worker are the lowest in GAME, the proportion of GVA converted into profit is the highest in GAME followed by NDME and DME. The GVA and Profit per worker in all the three types of enterprises is depicted in Figure 4.1.

52

Figure 4.1: GVA and Profit per Worker (Rs.) in Agro and Non-agro Enterprises across Types of

Enterprise (OAME, NDME, and DME)

A. GVA per worker B. Profit per worker

30000- IB Agro • Non-agro {

^ 25000 CO

^ 20000-

1 1 15000 k

8 ^ 10000 -

> a

5000

• ^ 25000 CO

^ 20000-

1 1 15000 k

8 ^ 10000 -

> a

5000 1 i VT^^M _m -•_ OAME NDME DME

30000

^ 25000 0)

f-20000

I I 15000 w £•10000 •s a 5000

B Agro • Non-agro

lalri OAIVE NDIVE DtVE

From Figure 4.1 it is evident that the GVA per worker is almost equal in NDME and DME for agro as well as non-agro enterprises, while it is much lower in the OAME. The profit per worker, however, is the highest in NDME and the difference between profit per worker across NDME and DME is higher in agro enterprises in comparison with that of the non-agro enterprises. In fact the profit per worker in the agro enterprises in DME is even lower than that in the OAME.

One of the structural reasons of lower profit per worker in the agro enterpr ises in the DME category may be comparatively higl3,er concentration of enterprises of seasonal nature ih this category. However, profit per worker is also lower in DME mainly because of high wage and other factors pajnnents shares to the value added. It can be seen from Table 4.3 that the total share of non-profit factor payments to the GVA, in the DME group, is more than 56 per cent. This is approximately 60 per cent in the case of non-agro and approximately 52 per cent in the agro enterprises. The wage share alone accounts for 50 per cent and 45 per cent in non-agro and agro enterprises respectively in the DME group. In the NDME group the wage share is approximately 35 per cent and the total non-profit factor payments is approximately 40 per cent. In contrast to these, the non-proht factor payments is less than 2 per cent in the OAME category of enterprises with no significant difference in the pattern across the agro and non-agro enterprises (Table 4.3).

53

Table 4.3 : Proportion of Non-profit Factors Payment to Gross Value Added

Types of enterpr ise Proportion (%) of non-profit factors payment Types of enterpr ise

Emolumen t s Rent Interes t All

OAME

Agro 0 .43 0.62 0.58 1.64

Non-agro 0.44 0.71 0.51 1.66

All manufac tur ing 0.44 0.64 0.57 1.64

NDME

Agro 34.10 1.95 2.42 38 .46

Non-agro 39 .32 3.43 2 .28 45 .03

All manufac tur ing 35 .79 2 .43 2 .37 40 .59

DME

Agro 45.17 0.65 • 5.96 51.78

Non-agro 50.50 1.82 6.66 58.98


AU

Agro 10.74 0.81 1.52 13.06

Non-agro 29.31 1.62 3.62 34 .55


Table 4.3 shows that, in general, the proportion of non-profit factor pa5nments to GVA is higher in DME and in non-agro enterprises. The proportion is lower in NDME and in agro enterprises. This leads to h igher r a t e of t rans format ion of GVA into profit in t he agro enterprises particularly in NDME group. In the non-directory agro enterprises, profits account for more than 60 per cent of GVA as against less than 50 per cent in directory agro enterprises. Because of negligible share of non-profit factor pa5nments to GVA in OAME group, the rates of GVA and profit are almost equal. Overall, the share of non-profit factor payments to GVA in the agro enterprises is 13 per cent, which is approximately one-third of that in the non-agro enterprises.

2. Fixed Asset

In order to see role of capital and technology use, total value of fixed asset has been taken as a proxy. Fixed assets^ are the assets held for the purpose of producing or providing goods or services and are

6. The components of Total Fixed Assets are a) plant and machinery, b) Transport and equipment, 3) Tools and other fixed assets, and d) Land and buildings.

54

not held for resale in the normal course of entrepreneurial activities. If we see the value of total fixed asset (including land) per enterprise and per worker, both are invariably much higher in the DME than in the other two categories. Further, both the ratios are higher in the non-agro than in the agro enterprises across all the three types of enterprises, le. OAME, NDME, and DME. Although the difference between agro and non-agro is not substantial in the OAME and NDME categories, in the DME category the value of fixed asset per enterprise is more than double and value of fixed asset per worker is higher by 50 per cent in the non-agro than in the agro enterprises (Table 4.4). This directly suggests that in the non-agro enterprises the use of capital is much higher than that in the agro enterprises. Moreover, since fixed assets also include plant and machinery, higher value of fixed asset per enterprise and per worker in the non-agro enterprises indirectly hints about higher use of technology in these enterprises in comparison with that in the agro enterprises. In the OAME, the value of fixed assets per worker is approximately half and one third of t ha t in DME category in agro and non-agro enterprises respectively, thereby suggesting much lower use of capital in general and technology in particular in the OAME category both for agro and non-agro enterprises.

Table 4.4 : Fixed Asset (including land) Per Enterprise and Per Worker and Ratio of GVA to Fixed Asset

Types of enterpr ise Fixed Assets Per Enterprise (Rs.)

Fixed Assets Per Worker (Rs.)

GVA/Flxed Assets

OAME

Agro 17,142 10,041 0.85

Non-agro 20 ,298 10,893 0.91

All manufac tur ing 17,634 10,184 0.86

NDME

Agro 81 ,422 26 ,917 0 .65

Non-agro 108,927 33 ,916 0.71

All manufac tur ing 88 ,146 28 .706 0.67

DME

Agro 214 ,484 22 ,341 0.76

Non-agro 472 ,568 31 ,770 0.79

All manufac tur ing 320 ,684 27 ,244 0.78

All enterprise

Agro 23 ,093 12,249 0.81

Non-agro 50 ,403 19,120 0.82

All manufac tur ing 27 ,621 13,743 0.81

55

The ratio of GVA to total fixed assets is lower in the agro enterprises in all the three categories, which hints that the use of capital per unit of GVA is higher in the agro enterprises in comparison with their non-agro counterpart, leading to a higher capital output ratio in these enterprises. However, it is amply clear that the higher capital output ratio in the agro enterprises arises not because of higher use of capital per worker but mainly because of lower value of GVA per worker in compar ison with t h a t in the non-agro enterprises. This is also clear by the fact that the ratio of GVA to the fixed asset is the highest in the OAME category in both the agro and non-agro enterprises where the use of the fixed asset per worker is the lowest.

In the total value of fixed assets at the enterprise level, the single largest component is land (owned or hired). The contribution of land to the fixed asse ts is more than 70 per cent in the OAME and NDME categories. In the DME category, the same is slightly more than 55 per cent. The contribution of land to the total fixed assets is higher in agro (58 per cent) than in non-agro enterprises (54 per cent) (Table 4.5).

Table 4.5 : Percentage Contribution of Different Components to Total Fixed Assets in Agro and Non-Agro Enterprises Across

Types of Enterprises

OAME NDME DME All types

Agro

Plant & machinery 17.43 24 .21 32 .97 20.68

Transpor t & Equipment 1.52 2 .06 5.96 2.21

Tools & other Fixed Asset 3.92 3.42 3.12 3.73

Land 77.13 70 .31 57 .96 73.38

Non-Agro

Plant & machinery 11.46 22 .37 34 .90 24.90

Transpor t & Equipment 1.94 3.06 8.53 5.40

Tools & other Fixed Asset 5.31 3.53 2.21 3.48

Land 81 .29 71 .03 54 .36 66.22

All Enterpr ises

Plant & mach ine iy 16.34 23 .65 34 .18 22 .00

Transpor t & Equipment 1.60 2.37 7.57 3.21

Tools & other Fixed Asset 4 .17 3.45 2 .55 3.65

Land 77.89 70 .53 55.70 71.13

56

The contribution of plant and machinery is higher in the agro enterprises in the OAME and NDME categories but lower in DME category in comparison with those in the non-agro enterprises. However, because of the size of the DME, the overall proportion of plant and machinery to the total fixed assets is lower in the agro enterprises as a whole.

The break-up of fixed assets in the agro industries at the two-digit level presents an interesting contrast across the industry group (Table 4.6). In the industry groups of tobacco and leather the proportion of land alone is more than 90 per cent. It implies that in these two indust ry groups use of technology, such as p lant & machinery, transport & other equipments, and tools & other fixed assets , is at lowest levels. In addition to Cotton ginning, which constitute miniscule proportion of agro enterprises, proportion of non land fixed assets is more than 30 per cent only in two industry groups viz. food products & beverages and textiles. These are followed by wearing apparel and wood and wood products where the proportion of non land fixed asset is slightly more than 20 per cent each. However, even these two industry groups proportion of plant and machinery is approximately 11 to 12 per cent.

Table 4.6 : Percentage Contribution of Different Components to Total Fixed Assets in Different Industry Groups of Agro

Enterprises

Sr. No.

Plant & machinery

Transport & equipment

Tools & other

equipments

Land Total

1 Cotton ginning, cleaning and bailing 38.02 0.92 2.03 59.03 100.00

2 Food products and beverages 29.37 2.78 3.67 64.19 100.00

3 Tobacco products 2.14 1.77 4.41 91.68 100.00

4 Textiles 20.41 2.03 2.64 74.92 100.00

5 Wearing apparel 11.19 0.67 3.43 84.71 100.00

6 Tanning and dressing of leather 3.64 0.85 3.89 91.63 100.00

7 Wood and wood products 11.82 2.86 5.74 79.58 100.00

8 Paper and paper products 35.35 4.30 2.75 57.60 100.00

Overall within the agro enterprises only food processing and textiles are the two important indust ry groups where use of plant and machinery and transport and equipment are present in sizeable proportion. In the DME category the proportion of these technical

57

fixed a s se t s reaches u p to more t h a n 40 per cent, p lant and machinery alone constitutes approximately 30 per cent of the total fixed assets in these two industry groups in the DME category. The break up of fixed asse ts in the above mentioned three types of enterprises is presented in Appendix I.

When we take value of fixed assets as the non-land value at the enterprise level, the difference between agro and non-agro enterprises widens in comparison to the ratios calculated on the basis of total fixed assets (including land). The value of fixed asset per enterprise in agro is approximate ly one th i rd to t h a t in t he non-agro enterprises. Similarly the value of fixed assets (non land) per worker in the agro is approximately half of that in the non-agro enterprises. However, in the OAME category the situation is not different between agro and non-agro enterprises with slightly higher value in the agro enterprises (Table 4.7).

The widest difference between agro and non agro enterprises emerges in the DME category, where use of fixed assets per enterprise is more than double in the non agro. The difference is, however lower in terms of fixed asset per workers.

Table 4.7: Value Per worker in

of Non-Land Fixed Assets per Enterprise and Agro and Non-Agro Enterprises in Different

Categories.

Types of enterprise Per Enterprise (Rs.] Per Worker (Rs.) Fixed Assets/GVA OAME Agro 3.920 2,296 0.27 Non-agro 3,799 2.039 0.21 All manufacturing 3,899 2.252 0.26 NDME Agro 2,4175 7,992 0.46 Non-agro 31.551 9,824 0.42 All manufacturing 25,975 8.459 0.45 DME Agro 90,175 9,393 0.55

Non-agro 215,672 14,499 0.63 All manufacturing 142,049 12,068 0.60 All enterprise Agro 6,147 3,260 0.33 Non-agro 17.026 6,459 0.44 All manufacturing 7,973 3,967 0.36

58

Within the agro enterprises, the value of fixed assets per enterprise as well as per worker is the highest in Cotton ginning, cleaning and bailing followed by Paper and paper products and the lowest in Tobacco products followed by Wood and wood products. Although some of the NDME enterprises from the industry group of Food products and beverages. Tobacco products, and Wearing apparel show fairly higher use of fixed asset, because of their very high concentration mainly in the OAME category, the overall value of fixed asset in these enterprises is much lower in comparison to that in the other industry groups such as cotton and paper (Table 4.8).

Table 4.8: Fixed Asset (excluding land) Per Enterprise and Per Worker and Ratio of GVA to Fixed Asset in Different Industry

Groups within the Agro-Enterprises Sr. No.

Indus t ry group OAME NDME DME All Sr. No. Fixed Assets Per Enterpr ise

1 Cotton ginning, cleaning and bailing 12,224 24 ,916 827 ,745 39 .428

2 Food produc ts and beverages 11,126 38 .034 92 ,946 15,580

3 Tobacco p roduc t s 338 7,513 21 ,613 646

4 Textiles 2 ,969 15,904 100,257 6.281

5 Wearing appare l 3,131 9.546 36 ,767 3,702

6 Tanning a n d dress ing of leather 1,328 8,199 31 ,848 1.794

7 Wood a n d wood products 1,265 25 ,228 165,370 2 ,332

8 Paper and pape r products 4 7 3 222 ,969 756.591 21 ,193

9 Fixed Asse ts Per Worker

10 Cotton ginning, cleaning and bailing 8,870 9.439 31 ,985 17.264

11 Food p roduc t s a n d beverages 5,830 12.841 10.361 7 .112

12 Tobacco produc ts 221 1,972 1,871 388

13 Textiles 1,493 4 ,560 9 ,764 2 .740

14 Wearing appare l 2 ,487 3,593 5,037 2 .684

15 Tanning and dress ing of leather 989 2 ,608 3,544 1.226

16 Wood and wood products 716 8,205 22 ,034 1.284

17 Paper and pape r products 229 51,241 63 ,957 9 .114

18 GVA/Flxed Assets

19 Cotton ginning, cleaning and bailing 0.84 1.52 0.56 0.70

20 Food p roduc t s and beverages 1.89 1.41 1.56 1.72

21 Tobacco p roduc t s 33 .33 7.69 4 .76 16.67

22 Textiles 4 .76 3 .03 1.96 3 .33

2 3 Wearing appare l 4 .17 4.55 3.03 4.17

2 4 Tanning a n d dress ing of leather 14.29 10.00 7.14 12.50

2 5 Wood a n d wood products 10.00 2.86 1.79 6.67

2 6 Paper a n d paper p roduc ts 12.50 0.50 0 .76 0 .98

59

The ratio of GVA to fixed asset presents an interesting variation across the industry groups within the agro enterprises. The ratio is significantly higher in almost all the labour intensive industry groups, where the use of capital is minimal. As for example, in the industry groups such as Tobacco products. Tanning and dressing of leather, and Wood and wood products, the ratio of GVA to the fixed asset is more than 6 as against 0.98 in the paper industry and as low as 0.70 in the cotton industry. In the Tobacco products, and Tanning and dressing of leather the ratios are more than 4.0 even in the DME category, thereby signifying very low use of capital in general and technology in particular.

Accordingly, it can be calculated that capital output ratio (K/Oy is the highest in the Cotton ginning, cleaning and bailing followed by Paper and paper products and Food products and beverages. It is interesting to note that within the agro enterprises, the industry groups of Food products and beverages and Textile show higher K/ O than in the Wearing apparel (Figure 4.2).

Figure 4.2 : Capital Output Ratio (K/O) in Different Agro Industries

0.20 I • • Paper Food Textiles Wearing Wood leather Tobacco

products apparel products

Higher value of K/O ratio in the Food products and beverages and textiles industry groups arises mainly because of much lower value of GVA in relation to the capital and more specifically technology used in these industries. In contrast to these the industry groups such as Tanning and dressing of leather. Wood and wood products, and Tobacco products use mainly labour intensive technology and hence the K/O ratio is much lower in these industry groups.

7. Capital output ratio (K/O) has been calculated in terms of ratio of fixed asset to GVA (Fixed asset/GVA)

60

3. Receipts and Expenses

Total receipt of the enterpr ise is defined a s the sale value of products and by products manufactured by an enterprise together with the value of services rendered to other concerns and other receipts incidental to entrepreneurial activities. This is comprised of mainly three types of receipts: (a) manufacturing receipts, (b) trading receipts, and (c) other receipts^. Total receipt per worker is the highest in the directory enterprises followed by non-directory and own account enterprises. In the OAME the total receipt per worker is less than one-fifth of that in the DME both for the agro and non-agro enterprises. As in the case of GVA, within each category, the total receipt per worker is lower in agro enterprises than in the non-agro-enterprises. The difference is the widest within the NDME. In the NDME category the total receipt per worker is approximately 50 per cent higher in the non-agro enterprises in comparison with that in the agro enterprises (Table 4.9).

Table 4.9 : Per worker Receipt and Expenses and Percentage of Expenses to Receipts

Per worker Percentage of

Total receipt

Total expense

Manufactur ing

expense

Total expense to total receipt

Manufactur ing Expense to

total expense

OAME Agro 17,342 8,762 6 ,415 50 .53 73 .21

Non-agro 17,753 7,790 5,060 43 .88 64 .95 Manufac. 17,411 8,599 6 ,188 49 .39 71 .96

NDME

Agro 41 ,642 24 ,198 17,064 58 .11 70 .52

Non-agro 63 ,566 39 ,220 29 ,451 61 .70 75 .09

Manufac. 47 ,248 28 ,039 20 ,231 59 .35 72 .15

DME

Agro 72 ,629 54 ,950 46 ,125 75 .66 83 .94

Non-agro 76 ,463 50 ,444 27 ,248 65 .97 54 .02

Manufac. 74 ,622 52,607 36 ,309 70.50 69 .02

AU

Agro 23 ,313 13,378 10,182 57 .38 76 .11

Non-agro 39 ,100 23 .123 13,798 59.14 59 .67

Manufac. 26 ,746 15,497 10,968 57 .94 70 .78

8. These are respectively defined as (a) receipts generated from sale of main manufactur ing items of the enterpr ise , (b) receipts from trading of the manufactured items, and (c) receipts generated from other than manufacturing activity of the enterprise.

61

Overall, per worker total and manufacturing expenses are lower in the agro enterprises than in the non-agro enterprises. However, both are much higher in the agro enterprises both in the OAME and DME categories. It is only in the NDME category that these ratios are lower in the agro enterprises. Further, in all the three groups of enterprises, it is per worker manufacturing expense, which causes the m a x i m u m difference between the agro and the non-agro enterprises. Although the share of manufacturing expenses is higher in agro than in non agro enterprises, the difference between the two is lower in the NDME category as compared with OAME and DME categories. The higher share of manufacturing expenses to total expenses can be explained in terms of predominance of labour intensive techniques and seasonal nature of the agro enterprises particularly in the DME category.

Accordingly, wi th in the NDME, agro en te rp r i se s show lower proportion of total expenses in total receipt as well as manufacturing expenses to total expenses in comparison with those in the non-agro enterprises. In the other two categories, these two proportions are significantly higher in the agro enterprises. However, this also suggests that the proportion of non-manufacturing expenses in the non-directory agro enterprises is higher in comparison with that in the non-directory non-agro enterprises. These expenses include costs incurred on account of trading, services rendered to other concerns which are incidental to the manufacturing activities, etc.

If we see the proportion of manufacturing and operating expenses to only manufac tu r ing receipt, we find t ha t bo th the ra t ios are substantially higher in the agro in comparison with that in the non-agro en te rp r i ses . In the directory category, the proport ion of manufacturing expenses in the manufacturing receipt is as high as 72 per cent , while the propor t ion of opera t ing expense is approximately 85 per cent in the agro-enterprises. Similarly, in the agro en te rp r i ses the proport ion of operat ing expenses in the manufacturing receipt is almost 90 per cent and 85 per cent in the NDME and OAME categories respectively. It is worth noting that in the DME category the proportion of manufacturing expense in the total receipt is less than 40 per cent in the non-agro enterprises. Similarly, the operating expense in the directory non-agro enterprises is approximately 71 per cent. The proportion of operating expense is, in fact, the lowest in the OAME for the non-agro enterprise. This implies that in the OAME category proportion of non-manufacturing expense (such as trading expense) is lower in comparison with other types of enterprises, both for agro as well as non-agro enterprises.

62

Taking all the enterprises together the proportion of manufacturing expense in total receipt is approximately 43 per cent in the non-agro as agains t 67 per cent in the agro enterpr ises . In the case of operating expense, it is 71 per cent in non-agro and 86 per cent in the agro enterprises (Table 4.10).

Table 4.10 : Proportion of Manufacturing and Operating E2cpense to Total Receipt

Types of enterpr ises Per cent to manufac tur ing receipt Types of enterpr ises

Manufactur ing expenses Operat ing expenses

OAE

Agro 63 .89 85.11

Non-agro 43 .87 65 .75

Manufactur ing 60 .13 81 .48

NDME

Agro 66 .23 89 .72

Non-agro 59.54 76 .87

Manufactur ing 63 .57 84 .61

DME

Agro 72 .22 84 .98

Non-agro 38 .83 71.44

Manufactur ing 54 .07 77.62

All enterprises

Agro 66 .79 85 .67

Non-agro 42 .96 70 .98

Manufactur ing 57.99 80 .24

The percentage of total expense in total receipt by the breakdown of agro enterpr ises into different industry groups shows t ha t the percentage is the lowest in the industry group of 'wearing apparel' followed by 'tobacco products' and 'wood and wood products'. The proportion is also substantially lower in the 'textiles' group. In 'food products and beverages' and 'paper and paper products', the share of expense is as high as three-fourths of the total receipt of the industry. In the own account enterprises, all the industry groups (par t icu lar ly tobacco p r o d u c t s and wear ing appare l ) have comparatively lower proportion of expenses to the total receipt. In the industry groups such as 'tobacco products' the share of expenses in receipt is as low as 14 per cent. In contrast to this, the share is as high as 83 per cent in 'food products and beverages' in the directory group (Table 4.11).

63

Table 4.11 : Proportions of Total Expense to Total Receipts and Manufacturing Expense to Total Expense in Different

Industry Groups of Agro Enterprises

Sr. No.

Per cent of total expense to total receipt

Per cent of manufactur ing expense to total expense

Sr. No. GAME NOME DME All GAME NOME DME All

1 Cotton ginning, cleaning and bailing

56 .23 83.40 64 .57 67 .87 39 .56 93 .36 56 .08 65 .66

2 Food produc ts a n d beverages

70.30 69.36 82.72 73.18 75.91 71.27 88 .18 78.67

3 Tobacco p roduc t s

13.88 61.90 77.02 36 .38 50.12 60 .75 74.66 67.26

4 Textiles 29 .06 41 .86 57.91 40 .85 64 .88 71.90 71 .43 69 .06

5 Wearing apparel 26 .55 30 .73 48 .26 28 .13 45 .84 53.41 56.62 48 .18

6 Tann ing and dress ing of lea ther

54 .65 64 .10 43 .55 55 .47 85 .15 92 .21 84 .28 86 .37

7 Wood and wood p roduc t s

30 .00 51.84 75.02 39.60 75.38 74.57 88 .76 78.99

8 Paper and pape r p roduc t s

53 .76 60.39 77.42 72 .45 82 .35 87.62 83 .88 83 .87

9 All agro 50 .53 58 .11 75.66 57 .38 73 .21 70.52 83.94 76.11

The industry groups, viz. Tobacco products, Textiles, and Wearing apparel are efficient in terms of lower proportion of total expense in total receipt but these have higher proportions of non-manufacturing expense in to ta l expense . These i n d u s t r y g roups have non-manufacturing expenses varying from 30 per cent in the case of textiles to more than 50 per cent in the case of wearing apparel. This suggests that these industry groups are engaged in many other activities apart from the manufacturing of the main products. One of the reasons of high proportion of non-manufacturing expenses in these enterprises may be high proportion of putting out system in t h e s e e n t e r p r i s e s . These en t e rp r i s e s i n c u r smal l a m o u n t of manufacturing expenses, because generally they do not purchase raw mater ials of their own. As for example within the wearing appatrel enterprise most of the enterprises are engaged in the work of stitching dress materials and these enterprises incur cost mainly on the heads of threads, buttons etc. In contrast to this they incur high cost on the front of electricity, t ransport , rent etc. This is also reflected by the fact that the proportion of non-manufacturing to

64

total expense is the highest in own account enterprises followed by non-directory and directory enterprises. However, in the DME group industries such as wearing apparel and Cotton ginning, cleaning and bailing, the share of the non-manufacturing expenses is more than 34 per cent of the total expenses.

Table 4.12 : Percentages of GVA, Profit and Variable Cost to Total Receipt

Types of enterpr ise % GVA to Receipts

% profit to Receipts

% emolumen t s to Receipts

% var. cost to Receipts

OAME

Agro 49 .31 46 .94 0 .21 50 .90

Non-agro 55 .93 53 .13 0 .25 44 .31

All manufac tur ing 50.44 48.00 0.22 49 .78

NDME

Agro 41.70 25 .33 14.22 72.52

Non-agro 38.01 20 .15 14.94 76 .93

All manufac tur ing 40 .43 23 .55 14.47 74 .04

DME

Agro 23 .52 10.19 10.62 87 .11

Non-agro 32 .73 13.62 16.53 83 .80

All manufac tur ing 28.42 12.01 13.77 85 .34

AU

Agro 42.30 35.47 4 .54 62 .24

Non-agro 40 .03 25 .67 11.73 71 .70

All manufac tur ing 41 .58 32 .36 6 .83 65 .25

The percentage share of GVA and profit in the total receipt of the en te rp r i se s is h igher in the agro en te rp r i se s , which is more prominent in case of percentage of profit to total receipt (Table 4.12). However, these shares are higher in the agro enterprises only in case of non-directory enterprises. In the own account and the directory groups, the shares of GVA and profit are slightly lower in the agro enterprises in comparison with those in the non-agro enterprises mainly because of higher percentage of variable costs in the total receipts. The share of GVA and profit in total receipt is again the highest in the own account enterprises followed by the non-directory and directory enterprises both in the agro and non-agro enterprises on account of very low proportion of emoluments in t h e to ta l r ece ip t s . Given the p r e d o m i n a n c e of own a c c o u n t

65

en te rp r i ses in agro group and low proport ion in th i s type of en terpr i ses , because of the low proport ion of emolument , the propor t ion of overall variable costs is also lower in the agro enterprises in comparison with that in the non-agro enterprises.

As far as the total variable cost is concerned, it is lower in agro enterprises only in the case of NDME. In the own account and the DME groups, the percentage of total variable costs to the total receipt is higher in the agro enterprises. This is despite the fact that the proportion of wage cost in the total receipt is lower in the agro enterprises. This implies that the share of non-wage cost in the total receipt is much higher in the agro enterprises in comparison with that in the non-agro enterprises. The difference is much sharper in the own account enterprises. This is also because of very high raw material intensity in the agro enterprises.

Table 4.13 : Percentage Distribution of Total Receipts by Manufacturing, Trading and Other Receipts in Agro and Non

Agro Enterprises.

Types of enterpr ise Percentage distr ibution of total receipt by Types of enterpr ise

Manufactur ing receipt

Trading receipt Other receipt

OAE

Agro 57 .89 1.59 40 .52

Non-agro 64.96 1.59 33.44

Manufactur ing 59.10 1.59 39.31

NDME

Agro 61 .88 3 .15 34 .97

Non-agro 77.82 2 .37 19.81

Manufactur ing 67.36 2 .88 29 .76

DME

Agro 87.94 1.04 11.02

Non-agro 91 .78 0 .63 7.59

Manufactur ing 89 .99 0.82 9.19

All enterprises

Agro 65.40 1.68 32 .93

Non-agro 82 .13 1.17 16.70

Manufactur ing 70.72 1.51 27 .77

We have seen above that the total receipt of the enterprises includes other receipts incidental to entrepreneurial activities. To put it in

66

simple words, these receipts accrue to enterprises as a result of sale of other goods and services by the enterprise. The proportion of such receipt is less than 10 per cent in the directory enterprises because in the directory manufacturing category, very few enterprises are engaged in services which are inc iden ta l to the i r m a i n entrepreneurial activities. By contrast , the proportions of such receipts are substantially high in the case of non-directory and own account enterprises (Table 4.13).

Accordingly, the proportion of manufacturing receipts in the total receipts in these enterprises is smaller in comparison with that in the directory enterprises . In the NDME and OAME groups the proportions are as high as 30 per cent and 40 per cent respectively. However, even within the NDME and OAME, this proportion is much lower in the non-agro enterprises as compared to that in the agro enterprises. The agro enterprises have significantly higher share of other receipts also in the DME category. If we take trading receipt and other receipt together as non-manufactur ing receipts , the difference between the agro and non-agro enterprises is the widest in the NDME category (Figure 4.3).

Figure 4.3: Percentage of Non-manufacturing Receipts to Total Receipts in Different Enterprises

The high proportion of other receipts in the total receipts implies veiy high dependence of agro enterprises on receipts from the sale of those goods and services which are not their main products. This

67

situation arises because the production activities of many of the agro enterprises, largely from the own account category, are seasonal in nature and these enterprises get involved in many other activities during the off seasons. Besides this, many enterprises are much more diversified in the i r activit ies in compar i son with thei r classifications on the basis of the main products. This is also worth noting that many of the enterprises, particularly from the NDME group , a re a lso engaged in t r ad ing act ivi t ies , which yields approximately 3 per cent of the total receipt in this category.

The proport ion of other receipt is significantly high in all the industry groups within the OAME agro enterprises ranging from more than 90 per cent in the wearing apparel to 10 per cent in Tanning and dressing of leather. However, even within the directory group the industry groups such as wearing apparel and cotton ginning, cleaning and baiUng have the proportion of other receipts as high as 60 per cent of the total receipt. It is worth noting that within the NDME group only three industry groups, viz. Wearing apparel. Textiles, and Wood and wood products have more than 30 per cent as other receipt (See Appendix II). However, the share of other receipts in total receipts in the overall NDME is significantly higher than in the DME.

Within the agro enterprises, the proportion of other receipt is the highest in wearing apparel industry group, which is more than 92 per cent of the total receipt of the industry. The manufacturing receipt is only to the extent of approximately 6 per cent. Such a high proportion of other receipt in this indust ry group clearly signifies that there are a number of activities in which this industry group is involved in and these are not considered as a part of its main manufacturing activity. The proportion of other receipt in total receipt is also substantially high In the industry groups such as cotton ginning, cleaning and bailing, tobacco products, and textiles. In all these industry groups the proportions are more than 50 per cent. In contrast to these the industry groups such as paper and paper products, tanning and dressing of leather, food products and beverages, and to a great extent wood and wood products earn high proportion of receipts from the sale of their main products (Table 4.14).

68

Table 4.14 : Percentage Distribution of Total Receipt by Manufacturing, Trading and Other Receipts

Sr. No. Types of enterprise

Percentage distr ibution of total receipt by

Sr. No. Types of enterprise

Manufacturing receipt

Trading receipt

Other receipt

All enterprises

1 Cotton ginning, cleaning and balling 43 .23 4 .44 52 .33

2 Food p roduc t s and beverages 80 .59 2.19 17.22

3 Tobacco produc ts 43 .11 1.43 55 .45

4 Textiles 49 .84 0.12 50 .04

5 Wearing apparel 5.64 2.16 92 .19

6 Tanning and dress ing of leather 87 .48 2 .10 10.42

7 Wood and wood produc ts 65.02 1.05 3 3 . 9 3

8 Paper a n d paper products 96 .35 0 .15 3 .50

In Table 4.15, the share of other receipts in total receipts and profit rate (net surplus/total receipts) are presented for all the village level enterprises and OAME, NDME and DME respectively. At the all enterprise level, other receipts constitutes more than half of total receipts for three two-digit level out of eight two-digit level. These are tobacco processing, apparel and wood products. At the agro-industry level it constitutes more than one-third of the total receipts. At the non-agrb ihdustry level, the sHare of other receipts is comparatively smaller, i.e. one-sixth of total receipts is reported to be from other r ece ip t s . As expected, b e c a u s e of scale of opera t ion and commercialization of production process, the shares of other receipts are the largest in OAME, NDME and DME in tha t order a t the overall agro-industry level. However, the share of other receipts falls substantially at the agro-industry level as one moves from NDME to DME size class. At the two digit level, however, the share of other receipts is substantially high in the case of wearing apparel in all size classes. But in tobacco processing, only in OAME size class, manufacturing receipts is completely dominated by other receipts which are not found in other larger size classes.

69

Table 4.15 : Percentage Other Receipts and Surplus by Size Class of Enterprises

Sr. No.

Industry group

All Enterprises

GAME NOME DME

Sr. No.

Industry group

% other Receipt

% surplus

% other Receipt

% surplus

% other Receipt

% surplus

% other Receipt

% surplus

1 Cotton ginning, cleaning and balling

52.33 23.56 80.62 41.83 15.52 11.69 59.83 21.58

2 Food products and beverages

17.22 22.02 21.58 27.52 22.50 20.46 3.09 8.85

3 Tobacco products

55.45 57.92 85.92 84.52 15.87 20.28 1.47 11.42

4 Textiles 50.04 44.13 60.63 67.72 48.93 31.81 34.77 13.67

5 Wearing apparel

92.19 61.96 95.16 69.30 86.05 41.77 62.68 21.13

6 Tanning and dressing of leather

10.42 38.48 9.93 43.16 2.59 20.30 38.37 22.70

7 Wood and wood products

33.93 52.96 39.20 67.34 32.85 25.59 9.33 8.15

8 Paper and paper products

3.50 12.70 10.97 44.34 0.24 9.58 2.12 6.03

9 Agro 32.93 35.47 40.52 46.94 34.97 25.33 11.02 10.19

10 Non-agro 16.70 25.67 33.44 53.13 19.81 20.15 7.59 13.62

All 27.77 32.36 39.31 48.00 29.76 23.55 9.19 12.01

One interesting finding is that there seems to be direct correlation between the share of other receipts and the rate of profit at the two digit level. The higher the share of other receipts, the higher the rate of profit. However, profit rate seems to be disproportionately higher in case of the OAME size class. The major reason behind it is that, it does not employ any hired labour on long-term basis and is largely run on unpaid family labour. Similar is the case, to a certeiin extent in respect of NDME enterprises.

70

Box I : Other Receipts of Enterprises

One important aspect of rural manufacturing is job work. It is a kind of system like putting out system which was prevalent in the initial stages of industrial revolution in industrialized rnnntries. This is undertaken to avoid two kinds of problems that the vllluye level enterprises face. Firstly, raw materials are to be procured from distant places or raw materials which are perishable in nature. In addition, agriculturcd products are seasonal in nature and so obtaining raw materials during season and utilizing these raw materials over the whole year/ several months in a year may be a better and cheaper option but the working capital constraints in procuring large amount of raw materials at one go may be a major problem of the village level agro-industries. Secondly, there exists problem of marketing. Village industr ies being small in operation, find it dilBculty to sell produce in distant places/or even in local areas directly due to increased competition over time and lack of marketing network. In the 56'" round of National Sample Survey (2000-01) the aspect of job work is captured through 'receipts from services provided to others including commission charges'. A large part of these receipts are 'job works' basically referred to as 'rendering services'. [What is important is to break block 4.1 into Items 441 to 449 showing importance of item 441 in 'other receipts and services'. It has to be shown from 51s t round by adding up i tems because in 56'" round breakdown are not given in uni t data] . In the 51^' round, the shares of remuneration received for work done for other concerns in total receipts for agro-industry as a whole, OAME, NOME and DME are 27.46, 30.39, 32.41 and 17.02 per cent respectively.

Job work is, thus , seen to be an important aspect of rural agro-process ing act ivi t ies and it is con t r ibu t ing posit ively to the sustenance of the village level agro-processing activities.

4. Access to Credit and Credit Institutions^

Access to credit and credit institutions plays a crucial role in the overall growth of the manufacturing sector. Besides financing for fixed capital requirements, the credit institutions are also involved In financing for working capital to the manufacturing enterprises. The 56* round NSSO does not provide information on financing for fixed and working capital separately, but it does provide information on total magnitude of outstanding loans to the manufacturing sector. The data from this survey shows that the total estimated amount of loan ou t s t and ing to the unorganized manufac tu r ing sector is approximately Rs. 3500 croresi° in the year 2000-2001. The total amounts of loans outstanding to the agro and non-agro enterprises a re Rs. 1870 crores and Rs. 1633 crores respect ively . The

9. Paucity of da ta on loans h a s res t r ic ted u s to consider spread of loan outstanding as access to credit market.

10. This is estimated amount of loans standing on the basis of the coverage of the NSSO 56th round and hence may be somewhat underestimated in comparison to the bankers' statistics of the total loans outstanding.

71

proportional distribution of total loans by industry group within the agro enterprises is presented in Figure 4.4.

Figure 4.4: Proportional Distribution of Total Loans by Industry Groups within the Agro Enterprises

Leather 0.35% "

Wood ^^f' 8% 2% Cotton

"0.39%

Wearing apparel 4% "^ ^<; \ . \ 1

. Food products ~~~~ 47%

Textiles / 37%

X^Tobacco 2%

It can be seen from Figure 4.4 that out of total loan outstanding of Rs. 1870 crores to agro enterprises, 47 per cent goes to only food p r o d u c t s followed by 37 per cent to text i les . These together constitute approximately 85 per cent of the total loan outstanding in the agro sector. After these two industry groups, wood and wood products is the only industry group within the agro enterprises, which is worth mentioning in respect of the proportion of loans outstanding (approximately 8 per cent).

Further, the overall penetration of the credit institutions in terms of coverage of enterprises all over the country has been abysmally poor. Out of the total number of 1 crore 20 lakh enterprises in the unorganized manufacturing sector, only 6.4 lakhs have access to credit and any type of credit institutions. Out of these enterprises, nearly 5.1 lakh uni ts are agro and 1.3 lakh uni ts are non-agro enterprises.

In terms of proportion, only 5.37 per cent of the enterprises has access to credit institutions. The proportion is slightly higher in the non-agro enterprises (6.59 per cent) than in the agro enterprises

72

(5.13 per cent) across all the three types of enterprises. Although the proportion reaches up to 28 per cent in the agro and 31 per cent in the non-agro enterprises in the DME category, in the OAME category the proportion is as low as 4 per cent (Table 4.15). Moreover, even with this low proportion of enterprises having access to credit institutions, informal credit institutions (comprising of professional money lenders, bus iness par tners , friends/relatives, suppl ie r s / contractors, etc.) cover more than half of these enterprises. The coverage of formal credit institutions has been extremely poor. In the OAME category, the coverage of the formal credit institutions has been less than 2 per cent. It is worth noting that in the OAME category, the coverage of informal credit institutions is approximately 50 per cent higher than that of the formal credit institutions. Even in the DME category, only 18 per cent of the enterprises has access to formal credit institutions. This proportion is 17 per cent in the case of agro and slightly more than 20 per cent in the case of non-agro enterprises. An additional 15 per cent of the DME enterprises takes loans from the informal sectors. Within the informal source, the coverage of moneylenders has been up to the extent of 30 per cent followed by friends/relatives (15 per cent), which is slightly less t han tha t of the formal source (43 per cent). The proportional distribution of enterprises by different sources of credit within the formal and informal sectors is given in Appendix III. In terms of total coverage, the moneylenders cover more than 1.6 per cent of all the enterprises while the formal source covers up to 2.3 per cent of the enterprises.

There sire very few enterprises which take loans from more than one source. Overall, the proportion is 0.25 per cent. In general, the non-agro en terpr i ses take loans from more t h a n one source more frequently. However, in the DME category the p ropor t ion of enterprises taking loans from more than one source is approximately 5 per cent. This proportion is approximately 7 per cent in the case of non-agro and only 4 per cent in the case of agro enterprises (Table 4.16).

If we see the proportion of enterprises within the agro group having access to credit institutions, we find that the only two industry groups, viz. Cotton ginning, cleaning and bailing, and Tanning and dressing of leather have more than 10 per cent of enterprises having access to credit institutions. Even within these industry groups, the Cotton ginning, cleaning and bailing has access to only other credit institutions not classified as formal or informal institutions. In the total proportion of 11 per cent of the Tanning and dressing of

73

Table 4.16 : Proportion of Enterprises Taking Loan from Different Sources

Item Formal Institutions

Informal In-Institutions

Others Total More than one source

Agro OAME 1.79 2.61 0.08 4.36 0.13 NOME 8.08 5.53 0.31 13.21 0.70 DME 17.14 14.45 0.47 28.41 3.65 Total 2.32 2.92 0.10 5.13 0.21 Non-agro OAME 1.65 2.33 0.11 4.05 0.04 NOME 13.09 6.70 0.25 18.70 1.34 DME 20.35 16.56 1.04 31.25 6.70 Total 3.50 3.40 0.17 6.59 0.48 All Industries OAME 1.77 2.56 0.09 4.31 0.11 NOME 9.30 5.82 0.29 14.55 0.86 DME 18.46 15.32 0.71 29.58 4.90 Total 2.51 3.00 0.11 5.37 0.25

leather having access to any credit institutions, only around 4 per cent is contributed by the informal sector. The industry group of Food products and beverages is also in the category which has comparatively bet ter access to credit inst i tut ions b u t is largely financed by the informal sector. The other industry groups such as Tobacco products. Wearing apparel. Wood and wood products, and Paper and paper products within the agro sector, have extremely poor access to any type of credit institution (Table 4.17).

Table 4.17 : Proportion of Enterprises from Different Industry Groups within Agro having Access to Credit Institutions

Sr. No.

NIC Two Digit Industrial Classification

Formal Institu

tions

Informal

Institutions

Others Total More than one

source

1 Cotton ginning, cleaning and bailing 2.49 0.57 12.02 15.08 0.00 2 Food products and beverages 3.82 5.37 0.15 9.13 0.22 3 Tobacco products 0.37 0.56 0.02 0.86 0.09 4 Textiles 2.37 4.38 0.06 6.36 0.45 5 Wearing apparel 2.35 1.50 0.18 3.94 0.09 6 Tanning and dressing of leather 8.47 4.10 0.02 11.27 1.32 7 Wood and wood products 1.89 2.09 0.07 3.91 0.14 8 Paper and paper products 2.10 0.41 0.02 2.41 0.13

Total 2.32 2.92 O.IO 5.13 0.21

74

In general, the coverage of formal credit institutions is less than half of the total number of enterprises within the agro sector having access to any type of credit institutions and the same is abysmally low in the case of industry groups such as Food products and beverages. Tobacco products. Textiles, and Wood and wood products. Moreover, it can be seen from Table. 4.17 that except two industry groups, viz. Tanning and dressing of leather and Food products and beverages in all other industry groups the coverage of formal credit institutions has been less than 3 per cent of the enterprises in these industry groups. Further , in two industry groups, viz. Tobacco products and Wood and wood products the proportion of enterprises having access to the formal credit institutions is barely 0.37 and 1.89 per cent respectively. Similarly, the industry groups of Textiles, Wearing apparel and Cotton ginning, the proportion of enterprises having access to formal credit institutions varies between 2 to 2.5 per cent.

Table 4.18 : Percentage Share of Different Types of Lending Institutions in Total Outstanding Loan

Formal Inst i tut ions

Informal Inst i tut ions

Others All

Agro

GAME 62.22 36.34 1.44 100.00

NOME 76.54 23 .05 0.41 100.00

DME 87 .43 12.41 0.16 100.00

Total 80 .84 18.72 0.44 100.00

Non-agro

GAME 65.62 33.31 1.07 100.00

NOME 80.29 19.34 0.36 100.00

DME 76.71 14.49 8.80 100.00

Total 76.41 16.00 7.59 100.00

All Indus t r ies

GAME 62.93 35.71 1.36 100.00

NOME 77.77 21 .84 0.39 100.00

DME 81.69 13.52 4 .78 100.00

Total 78.77 17.45 3.77 100.00

The con t r ibu t ion of the formal in s t i t u t ions to the to ta l loan outstanding is approximately 79 per cent while that of the informal

75

sector is 17 per cent. Approximately 4 per cent is contributed by other insti tutions. It is important to note here that although in terms of number of enterprises, the overall coverage of the informal sector is higher than that of the formal institutions, the contribution to the overall loans outstanding is 4.5 times higher for the formal institutions. This essentially signifies that the average amount of loan sanctioned by the informal institutions is much smaller than that by the formal institutions. However, it can be seen from Table 4 .18 that more than one-third of the total outstanding loans in the OAME category come from the informal institutions both for the agro and non-agro enterprises. In the DME category the contribution of the informal sector is 12 per cent in the case of agro and 14 per cent in case of non-agro enterprises.

The average size of ou t s t and ing loan, taking all these credit institutions together, comes to about Rs. 54.6 thousand per loan taking enterprise. Because of a very large proportion of enterprises not having access to credit and credit insti tutions, the average amoun t per enterprise comes to merely Rs. 2935 . In the DME category the average amount per enterprise is a little more than Rs. 105 thousand while for the NDME it is Rs. 7,256 and for the OAME it is merely Rs. 406. The average amount of loan going to the non-agro enterprises is four times higher than that going to the agro enterprises. Even within the DME category, the average amount of loan per enterprise is only Rs. 83 thousand in the case of agro enterprises as against Rs. 137 thousand in the case of non-agro enterprises. If we see these figures only for those enterprises who have taken loan from some institutions, the average size of loan to the non-agro enterprises is four times higher to the agro enterprises. This is mainly because of the very high concentration of the agro enterprises in the OAME category where the average size of the loan is very low. However, even the directory agro enterprises get much lower amount of loan in comparison with the directory non-agro enterprises. This difference is sharper in the case of informal sources of loans. The formal credit institutions practise less discrimination in this regard. In the OAME category, the formal institutions, however, are more biased than the informal institutions as the own account non-agro enterprises get almost 80 per cent higher amount of loan than that of the own account agro enterprises from the formal credit institutions (Table 4.19).

76

Table 4.19 : Average Amount of Outstanding Loan by Types of Lending Institutions

(Rs.)

' Per loan taking enterprise Per enter

pr ise ' Formal Inst i tu

t ions

Informal Inst i tut ions

Gthers All Institu t ions

Per enterpr ise

Agro

GAME 13.254 5.319 6,489 8.761 382

NOME 61 ,257 26 .937 8,613 48 .927 6 ,465

DME 423 ,761 71.362 27 ,256 292 .357 83 ,054

Total 65 ,579 12,044 8,223 36 .623 1,879

Non-agro

GAME 21 ,398 7.714 5.103 13,295 539

NOME 59,506 28 ,007 14.261 51 ,893 9 ,702

DME 516 .130 119,775 1,153,985 438 .088 136.887

Total 180.054 38 ,868 366,791 125.262 8.250

All Indus t r ies

GAME 14,438 5,658 6,216 9 .426 4 0 6

NOME 60 ,654 27 .238 9,781 49 .859 7 .256

DME 465 ,660 92 .902 709.807 355 ,710 105.206

Total 92 .031 17.083 98 ,472 54 .645 2 ,935

Within the agro enterprises, the lowest outstanding loan was found in the category related to tobacco products. This was followed by wearing apparel, and then by wood products. Tanning and dressing of leather was the next. In fact, in the OAME category, no industry group, except cotton and food products, has the average size of loan outstanding is more than bairely Rs. 500 per enterprise. Even in the DME category, the industry group such as tobacco products and tanning and dressing of leather has average size of loan outstanding less th£in Rs. 20,000 per enterprise. Similarly, wearing apparel in the DME category has the average loan outstanding as Rs. 28,100 per enterprise. It is only in the textile and cotton ginning industry groups tha t the average size of loan outs tanding is more t h a n Rs. 100,000 and Rs. 1000,000 respectively in the DME category (Table 4.20).

77

Table 4.20 : Average Amount of Loan Outstanding Per Enterprise across Different Industry Groups within

the Agro Enterprises

Sr.No GAME NOME DME All

1 Cotton ginning, cleaning and balling 1,500 - 1,044,700 34,400

2 Food products and beverages 1,000 9.500 69,800 3,600

3 Tobacco products - 1.000 16,600 200

4 Textiles 300 5,400 131,800 4,100

5 Wearing apparel 300 1,200 28,100 400

6 Tanning and dressing of leather 400 5,300 15,700 700

7 Wood and wood products 200 8,400 81,900 600

8 Paper and paper products - 294,500 521,700 16,500

Taking all the categories together (i.e. OAME, NOME, and DME), although the average size of loan outstanding in cotton ginning is Rs. 34 ,400 , the overall sha re of cot ton ginning In total loan outstanding is less than 0.4 per cent (refer Figure 4.4). In terms of percentage share, more than 85 per cent goes to only food products and textiles where the average size of loan is Rs. 3,600 and Rs. 4,100 respectively. All other industry groups stand very poorly both in terms of average size of loan outstanding and percentage share into total loans going to the agro enterprises.

The poor access to credit institutions and credit has led a large number of agro enterprises to divert from direct manufacturing activities. We have seen in the above section that these are the enterprises within the agro sector, which resort maximum to the 'put t ing out ' system in order to generate su rp lus . This can be directly linked to the overall availability situation of formal credit (or even any kind of credit) to these industry groups. Since these industry groups have poor access to the credit institutions in general and formal credit institutions in particular, these are not in a good position to carry on direct manufactur ing activities. The main problem they face in this regard is related to investment in capital and technology. Ultimately, these enterprises generate surplus and profits by engaging themselves in 'other' activities. These other activities are mainly 'putting out' system where these enterprises do not need to depend on the availability of credit.

78

Appendix I : Percentage Contribution of Different Components to Total Fixed Assets in Different Industry Groups of

Agro Enterprises

Plant & machinery

Transport & equipment

Tools & other equipments

Land Total

OAME

Cotton ginning, cleaning and balling 27.65 0.07 3.40 68.89 100.00

Food products and beverages 28.74 1.97 3.82 65.47 100.00

Tobacco products 0.10 0.43 4.61 94.87 100.00

Textiles 13.40 1.07 2.71 82.81 100.00

Wearing apparel 11.12 0.64 3.04 85.20 100.00

Tanning and dressing of leather 2.42 0.76 4.15 92.67 100.00

Wood and wood products 5.43 2.67 6.91 84.99 100.00

Paper and paper products 1.92 3.38 1.82 92.88 100.00

NDME




Textiles 24.91 1.67 2.34 71.08 100.00

Wearing apparel 10.38 0.77 5.12 83.73 100.00

Tcinning and dressing of leather 6.46 1.14 2.26 90.14 100.00



DME




Textiles 36.24 4.68 2.59 56.49 100.00

Wearing apparel 22.08 1.20 4.71 72.01 100.00

Tanning and dressing of leather 14.64 1.42 3.98 79.96 100.00



79

Aiêndix n : Percentage distribution of total receipt by Manufacturing, Trading and Other Receipts

Types of enterprise Percentage distribution of total receipt by

Types of enterprise

Meinufac-turing receipt

Trading receipt

Other receipt

OAME

Cotton ginning, cleaning and bailing 0.60 18.78 80.62

Food products and beverages 76.01 2.41 21.58

Tobacco products 13.75 0.33 85.92

Textiles 39.28 0.09 60.63

Wearing apparel 2.90 1.94 95.16

Tanning and dressing of leather 87.68 2.40 9.93

Wood and wood products 60.65 0.15 39.20

Paper and paper products 88.14 0.89 10.97

NDME




Textiles 50.69 0.38 48.93





DME




Textiles 65.16 0.07 34.77





80

Appendix m : Proportional Distribution of Enterprises Taking Loan from Different Sources

Item Main Institu

tions

Gther Institu

tions

Money Lender

Business Partners

Suppliers/

Contractors

Friends & Relatives

Gthers Total Loan taken from more than one

sources

AGRO

GAME 37.53 3.61 31.87 0.16 11.97 15.86 1.94 100.00 2.93

NOME 58.78 2.35 25.52 0.17 4.65 11.54 2.31 100.00 5.32

DME 56.29 4.03 30.43 0.06 4.18 16.18 1.67 100.00 12.84

Total 41.66 3.49 30.97 0.15 10.44 15.35 1.97 100.00 4.03

NON-AGRO

GAME 37.77 3.01 30.86 0.68 9.00 16.88 2.78 100.00 0.96

NOME 65.93 4.10 16.61 1.07 5.81 12.35 1.32 100.00 7.18

DME 61.25 3.86 25.78 2.26 6.75 18.21 3.34 100.00 21.45

Total 49.70 3.46 26.48 1.15 7.75 16.20 2.59 100.00 7.32

ALL INDUSTRIES

GAME 37.56 3.52 31.72 0.23 11.53 16.01 2.06 100.00 2.64

NOME 61.03 2.90 22.72 0.45 5.02 11.79 2.00 100.00 5.91

DME 58.45 3.96 28.41 1.02 5.30 17.06 2.40 100.00 16.58

Total 43.29 3.48 30.06 0.35 9.89 15.53 2.09 100.00 4.70

81

Appendix IV : Proportion of Enterprises Taking Loan from Any Source

Item Main Institu

tions

Gther Institu

tions

Money Lender

Business Partners

Suppliers/

Contractors

Friends & Relatives

Others From any

Source

AGRO

GAME 1.63 0.16 1.39 0.01 0.52 0.69 0.08 4.36

NOME 7.77 0.31 3.37 0.02 0.61 1.52 0.31 13.21

DME 15.99 1.14 8.65 0.02 1.19 4.60 0.47 28.41

Total 2.14 0.18 1.59 0.01 0.54 0.79 0.10 5.13

NON-AGRO

GAME 1.53 0.12 1.25 0.03 0.36 0.68 0.11 4.05

NOME 12.33 0.77 3.11 0.20 1.09 2.31 0.25 18.70

DME 19.14 1.21 8.06 0.71 2.11 5.69 1.04 31.25

Total 3.27 0.23 1.74 0.08 0.51 1.07 0.17 6.59

ALL INDUSTRIES

GAME 1.62 0.15 1.37 0.01 0.50 0.69 0.09 4.31

NOME 8.88 0.42 3.31 0.07 0.73 1.72 0.29 14.55

DME 17.29 1.17 8.40 0.30 1.57 5.05 0.71 29.58

Total 2.33 0.19 1.61 0.02 0.53 0.83 0.11 5.37

82

CHAPTER V

CONSTRAINTS AND VIABILITY OF AGRO-INDUSTRY AND GOVERNMENT PROGRAMME ON

RURAL INDUSTRIALISATION

This chapter is divided in several sections. We first describe the various constraints faced by the agro-industries. Then, we enumerate different Government programmes on rural industrialisation. Lastly, we examine the capacity utilisation and viability of agro-processing units and suggest required marketing framework.

I. Constraints of Agro-Industry

Agro-industry faces multiple constraints. We will discuss one by one.

1. Constraints of Raw Material and Marketing Products

In Regard to sourcing of basic input, the basic input is largely sourced from private enterprises and open market (table 5.1). More than one-third of enterprises each have reported to be sourcing them in the agro-processing sector. Not much difference is found among OAME, NDME and DME in this regard. Sourcing basic inputs from government and co-operatives is almost negligible — hardly one per cent of the enterprises are sourcing from them. However, nearly one-tenth of all OAME in the agro-industry source basic inputs primarily from contractor.

Table 5.1 : Distribution of Source Agency for Input Purchase

Distribution of Source Agency for Purchase of Basic Inputs

Type Govt. Co-operative

Private Enterprises

Contractor

Private Individual

No agency

Others

Agro OAME 0.85 0.44 32.54 9.36 17.95 33.57 5.30 Agro

NDME 0.22 1.51 48.15 2.80 14.20 30.74 2.38

Agro

DME 0.68 0.48 35.72 4.71 20.82 22.31 15.29

Agro

Total 0.82 0.49 33.33 8.98 17.81 33.27 5.30

Non-agro OAME 0.71 0.03 35.16 2.04 22.31 31.44 8.31 Non-agro

NDME 0.33 0.24 65.92 2.50 14.33 16.22 0.46

Non-agro

DME 2.23 0.17 51.53 5.69 20.51 15.01 4.86

Non-agro

Total 0.76 0.05 38.40 2.27 21.60 29.41 7.52

Manufacture Total 0.81 0.42 34.17 7.87 18.44 32.63 5.67

83

Even in the case of destination agency for sale of final product, government and co-operative bodies account for small proportion of enterprises (see table 5.2). However, as one moves from OAME to DME, the share goes up substantially, particularly for government agencies. Compared to OAME when only 0.18 per cent report selling of final products to government, in the case of DME the share is as high as 8.72 per cent. There exists substantial difference in case of major destination agencies across size group of agro-industries. Private individuals are the main destination agency for more than half of all en te rp r i ses in the agro-process ing sector . Private enterprises and contractors come distant second and third and around one-fifth each of all enterprises have reportedly sold primarily to t h e m . Within the agro-process ing u n i t s , the re is d is t inc t differentiation among OAME, NDME and DME. One-fifth of all OAME enterprises report sale through contractor and in case of NDME and DME, it is less than one-tenth. One-third of all NDME enterprises sell through private enterprises. In the case of DME, its share is overwhelming six-tenths. On the other hand, more than half of all OAME and NDME enterprises sell to private individuals but in the case of DME it is less than one-fifth. It seems that among the DME, contract in marketing of products through private enterprises is more prevalent than OAME and NDME. However, one-fifth of all OAME in agro-industry primarily sells to contractors where as in the case of DME it is less than one-tenth. It indicates that being larger sized they primarily sell to private enterprises bu t not on prearranged contract basis. As we have seen earlier, they sell relatively more to government as compared to OAME and NDME.

Table 5.2 : Distribution of Destination Agency for Sale of Final Product

Distributton of Destination Agency for Sale of Final Product

Type Government

Co-operatives

Private Enterprises

Contractor

Private Individual

Others

Agro OAME 0.18 1.08 21.22 20.40 55.09 2.02 Agro NDME 0.56 2.05 31.24 6.54 58.48 1.13

Agro

DME 8.72 3.45 59.26 9.72 17.67 1.18

Agro

Total 0.33 1.17 22.26 19.58 54.70 1.96 Non-agro OAME 0.00 0.12 17.96 7.07 73.98 0.86 Non-agro

NDME 0.50 0.12 29.74 3.50 65.32 0.82 Non-agro

DME 1.21 0.31 35.06 6.81 55.25 1.36

Non-agro

Total 0.10 0.13 19.76 6.78 72.34 0.89 Manufacture Total 0.29 0.99 21.85 17.47 57.62 1.79

84

Looking at the prevalence of contract work, it can be seen that a substant ia l portion of agro-processing uni t s works on contract. Working on contract is most prevalent among OAME (see table 5.3). Compared to agro-process ing un i t s , work on con t rac t is less prevEilent among the non-agro processing units. Working for contract is largely working for 'solely for enterprises'. More than four-fifths of agro-processing unit operating on contract basis works solely for larger enterprises. This practice is less prevalent among non-agro units.

Table 5.3 : Work on Contract

Type

Work on Contract

Type Yes No

Agro OAME 29.72 70.28 Agro

NDME 20.50 79.50

Agro

DME 23.66 76.34

Agro

Total 29.19 70.81

Non-agro OAME 18.79 81.21 Non-agro

NDME 24.57 75.43

Non-agro

DME 19.29 80.71

Non-agro

Total 19.27 80.73


Table 5.4 : T]rpe of Contract Work on Contract

Type Working solely for

Enterprises

Mainly on contract but also for other Customer

Mainly for Customer but also

on contract

Solely for

Customer

Agro OAME 84.61 5.97 3.77 5.65 Agro

NDME 68.66 14.79 7.46 9.10

Agro

DME 86.43 9.05 3.00 1.53

Agro

Total 84.09 6.30 3.88 5.72

Non-agro OAME 65.66 11.93 15.76 6.65 Non-agro

NDME 46.02 28.26 19.28 6.44

Non-agro

DME 61.27 21.02 10.56 7.16

Non-agro

Total 63.45 14.05 15.84 6.65

Manufacturing Total 81.69 7.19 5.30 5.82

85

The common practice in all types of contracts of agro-processing u n i t s is t h a t the equ ipment is mostly self-procured b u t raw materials and designs Eire mostly supplied by enterprises with whom sole contractual agreement is made. However, the degree differs to some extent between enterprises working solely on contract and enterprises working mainly on contract but also working for other customers (Tables 5.5 and 5.6).

Table 5.5 : Contract Solely for Enterprises

In Contract solely for Enterprise Raw materials

Supplied by Contractor

Design specified

Type Equipment Self-procured

Raw materials

Supplied by Contractor

Design specified

Agro GAME 91.26 96.39 93.63 Agro

NOME 91.51 90.61 90.68

Agro

DME 88.85 89.66 92.39

Agro

Total 91.24 96.15 93.54

Non-agro GAME 78.24 93.88 97.34 Non-agro

NOME 89.25 76.30 93.69

Non-agro

DME 86.08 83.36 88.58

Non-agro

Total 79.44 92.07 96.63

Manufacturing Total 90.19 95.79 93.82

Table 5.6 : Mainly on Contract

Mainly on Contract but also for other Customers

Type Equipment Self-procured

Raw materials Supplied by Contractor

Design specified

Agro GAME 88.12 70.53 86.30 Agro

NDME 78.69 59.87 69.15

Agro

DME 79.03 45.32 66.53

Agro

Total 87.21 69.25 84.61

Non-agro GAME 82.14 48.90 85.20 Non-agro

NDME 91.01 48.17 82.79

Non-agro

DME 97.62 39.12 64.04

Non-agro

Total 85.22 47.96 83.06

Manufacturing Total 86.77 64.56 84.26

86

The problem faced by enterprises in processing raw material and marketing of products gets directly confirmed when direct questions were asked regarding this . Nearly one-sixth of all en te rpr i ses complain about non-availability of raw materials, and no substantial difference exists in different categories of enterprises in this regard (see table 5.7). Among other problems, almost two-thirds of all agro-piocessing units do not mention facing any specific teething problem. Problems in marketing of products is referred relatively more by DME as compared to OAME and NDME in the agro-industry (Table 5.8). The problem of marketing is more endemic in the non-agro industry where they have to rely much more on selling to private individuals as compared to the agro-industries.

Table 5.7 : Non-availability of Raw Materials

Non-AvaUability of Raw Materials

Type Yes No


NDME 11.93 88.07

Agro

DME 15.17 84.83

Agro

Total 17.94 82.06


NDME 13.90 86.10

Non-agro

DME 20.25 79.75

Non-agro

Total 17.04 82.96


Table 5.8 : Problem in Marketing of Product

Problem of Marketing of Product

Type Yes No


NDME 17.70 82.30

Agro

DME 28.73 71.27

Agro

Total 19.01 80.99


NDME 30.58 69.42

Non-agro

DME 34.93 65.07

Non-agro

Total 32.33 67.67


87

2. Constraints of Electricity

Little more than one-tenth of all the village level agro-processing units complain about non-availability of electricity connection and power-cut (Tables 5.9 and 5.10). But there is distinct difference across various categories of agro-processing units in this regard. More OAME than NDME and DME complain about non-availability of electricity connection. But complaint about power-cuts is more prevalent among NDME and DME. Power-cut seems to be an endemic problem. One-third of all NDME and DME enterprises complain about power cut as opposed to one-tenth in the case of OAME. It shows the dependence of these categories of enterprises on power supply and complaints arising from it.

Table 5.9 : Non-availability of Electricity Connection

Nmi-AvailabtUty of Eiectrteitxj Connection

Type Yes No


NDME 9.84 90.16

Agro

DME 7.08 92.92

Agro

Total 13.50 86.50


NDME 9.30 90.70

Non-agro

DME 11.32 88.68

Non-agro

Total 14.38 85.62


Table 5.10 : Power Cut

TVpe

Power Cut

TVpe Yes No


NDME 31.72 68.28

Agro

DME 35.38 64.62

Agro

Total 13.19 86.81


NDME 30.43 69.57

Non-agro

DME 29.02 70.98

Non-agro

Total 11.68 88.32


88

3. Shortage of Capital

The major problem reported by agro-processing units is shortage of capital. Nearly half of all agro-processing units complain about shortage of capital (Table 5.11). However, complaint about shortage of capital is more severe in the non-agro units. Within the agro-processing units, all categories of enterprises complain about shortage of capital in almost similar proportion. Not much differentiation can be found in the case of non-availability of raw materials in all categories of enterprises both in the agro and non-agro enterprises.

Table 5.11 : Shortage of Capital

Shortage of Capital

Type Yes No


NOME 51.95 48.05

Agro

DME 50.69 49.31

Agro

Total 48.63 51.37


NDME 62.83 37.17

Non-agro

DME 55.62 44.38

Non-agro

Total 56.65 43.35


4. Other Constraints faced by Enterprises

Others mention various types of problems (see table 5.12). Most common but not in substantial proportion is the competition from the larger units and recoveiy of payment in that order. The DME complain relatively more about competition from larger units.

89

Table 5.12 : Other Problems

CO

o

Any Other Problem

Type Lack of other Infra.

facilities

Local Problem

Harassmen t

Competition from

Large Unit

Non-Availability of Labour

Labour Problem

Fuel N.A. or a t high Prices

Non-re-coveiy of Pay

men t

Others No Specific Problem

Agro GAME 2.59 6.67 1.84 7.63 0.20 0.68 1.59 6.04 6.28 66 .47 Agro

NOME 1.87 4 .89 0.09 10.55 0.98 2.11 0.24 7.62 7.58 64 .06

Agro

DME 1.34 4 .73 0.69 11.24 5.38 5.97 3.49 4 .75 9.46 52.94

Agro

Total 2.54 6.55 1.74 7.82 0.31 0 .83 1.56 6.10 6.39 66 .16

Non-agro GAME 3.21 8.30 0.19 6 .55 0.30 0.60 1.66 4.92 6.62 67 .65 Non-agro

NOME 2.48 4 .44 0.43 14.95 1.59 1.69 0.35 6.65 7.14 60 .28

Non-agro

DME 1.70 4.32 0.64 14.40 4.24 7.26 1.93 2.24 5.44 57.81

Non-agro

Total 3.08 7.79 0.24 7.61 0.60 1.02 1.57 4.92 6.60 66 .57

Manufacturing Total 2 .63 6.76 1.49 7.78 0.36 0.86 1.56 5.90 6.42 66.24

5. Multiplicity of Registration Authority

We have already seen earlier in chapter II that hardly 5 per cent of the agro-industry at the village level is registered. But, even these small proportions that are registered with multiple agencies. Almost two-thirds of them are registered with the Panchayat bodies and its occurrence is more in smaller size classes of OAME and NDME (Table 5.13). It seems that such overwhelming registration with village bodies is to take advantage of several government r u n schemes that are operated through the village Panchayat bodies. Another important agency of registration is SDI (State Directorate of Industries) which account for one-tenth of all registered enterprises. Expectedly, establishments (NDME and DME) are relatively more reg i s te red wi th them. Apar t from these agenc ies , t he re a re innumerable registration agencies as mentioned under 'other bodies' as well as under the 'not specified* category.

Table 5.13 : Distribution of Registration Agency in Size Classes of Agro-Industry

s ize

Class

Agency of Registration s ize

Class Panchayat Other Bodies SDI Not specified

OAME 66.06 7.39 8.64 17.90

NDME 73.40 3.11 13.27 10.20

DME 59.55 3.65 15.66 21 .13

All 67 .12 5.92 10.57 16.38

Note : Other bodies include KVIC. Coir Board. Silk Board. Jute Commissioner, Development Commissioner and Under Sec. 85 Factory Act.

6. Multiplicity of Ministries under which Village Level Agro-industries are Covered

One of the major problems faced by the village level agro-industries is that there is no single nodal agency which makes co-ordinated effort to develop this sector. Different sectors of agro-industries are governed by various ministries as enumerated below:

1. Ministry of rural and agro industr ies: KVIC. Coir Board and PMRY

2. Ministry of textiles: Handloom, powerloom, handicraft, sericulture and wool.

3. Ministry of food processing: Food processing industries.

91

4. Ju t e Commissioner: Jute .

5. Leather and wood based industries: Under Various Agencies.

n. Specific Government Programmes on Rural Industrialisation

There a re several Cen t ra l Government p r o g r a m m e s for the development of rural industrialisation.

1. National Programme for Rural Industrialisation (NPRI)

This programme was announced in 1999-2000 to promote clusters of units in rural areas. The aim was to set up 100 rural clusters each year. The Ministry of Agro & Rural Industries is designated to coordinate this programme with various ministries and agencies. The Small Industries Development Bank of India (SIDBI), National Bank for Agriculture and Rural Development (NABARD). Khadi & Village Indus t r i e s Commiss ion (KVIC) and the s t a t e s a re the major imp lemen t ing agenc ies of the p rog ramme. To faci l i tate the implementation of the programme, provision of extending financial assistance up to Rs. 5 lakh in each cluster exists. The purpose of this financial scheme is to fill in certain gaps in the development of the c lus ters . Under th is scheme, Khadi and Village Industr ies Commission was supposed to take up 50 rural industrial clusters for development during the year 1999-2000. SIDBI was also given responsibility of identifying 25 clusters for development. The rest were to be taken up by the Office of the DC (SSI), NABARD and the States.

KVIC has identified 50 clusters. Twelve clusters were taken up in 1999-2000, out of which 5 have commenced production. Further work on promotion of clusters for increasing rural emplo)rment and es tab l i shment of backward and forward linkage, set t ing u p of common facility centres , common service network suppor t for satellite cluster units, etc. have been taken up by KVIC.

2. Integrated Infirastuctiual Development (IID) Centres

This scheme is to be implemented in rural and backward areas of the country. The objective of the IID scheme is to create and develop infrastructural facilities like developed sites, power distribution network, water, telecommunication, drainage and pollution control facilities, roads, banks, raw materials, storage and marketing outlets, common service facilities and technological back up services. Under

92

this scheme, the Central Government provides aid in the form of grant up to Rs. 2 crores (Rs. 4 crores in the case of North-East regions) for a project with an investment of Rs. 5 crores. There is a provision of loan from SIDBI up to Rs. 3 crores b e c a u s e the estimated cost to set up an IID Centre is Rs. 5 crores (excluding cost of land) which is shared between the Government of India and the Small Industries Development Bank of India (SIDBI) in the ratio of 2:3. For the North-East region the share basis is 4:1. It excludes the cost of land to State Governments for setting up IID Centres. A project of the size of 15 to 20 hectares is expected to accommodate about 400 Small Industrial Units in rural and backward areas. The cost over and above Rs. 5 crores is borne by the State Governments which are expected to select appropriate site for the project and implement through an Implementing Agency of their own after getting the project appraised from SIDBI.

So far 58 IIDCs have been approved and the central grant of Rs. 38.83 crores has been released up to February 2001. Additional 50 clusters are proposed to be taken up in the lO"' plan period.

3. Assistance provided by KVIC for Setting up Units in Rural Areas

Since April 1995 KVIC has switched over to project approach and KVIC is providing margin money grant for establishment of industrial units in the rural areas. The grant component is 25 per cent of the project cost in normal cases. However, in the North East region, Andaman & Nicobar and Sikkim state this grant component of margin money is 30 per cent. It provides assistance to industries under its purview. The village industries are classified as followed:

Mineral Based Industry Forest and Agro Based Industry Polymer and Chemical Based Industry

Food Industry Handmade Paper and Fibre Industry Rural Engineering and Bio-Technology Industry SEP / Service Industry

Produc t ion of Khadi c loth h a s been on the dec l ine . Hence employment is a lso falling in Khadi . The ma in r e a s o n s a re uncertainty over continuation of rebate policy; high inventories; shortfall of availing funds from banks and budgetary resources and

93

Group I Group II Group III Group rv Group V

Group VI

Group VII

adjusting with project finance approach in place of pattern approach. Below the broad progress in the 9* Five-year Plan is presented :

KVIC 1997 2001-02

Production (Rs. Crores)

a. Khadi Cloth 624 432

b. Village Industries 3,895 5,914

Employment (million persons)

Khadi & village industries 5.65 6.02

(a) Bamboo & Cane Industries

The KVIC's developmental activities emphas i se br inging in of scattered artisans under the institutional framework of cooperative and registered ins t i tu t ions (and encouraging them to develop clusters) by providing them finance, improved tools and equipments and marketing facilities. The activity of this industry is prominent in the s ta tes of Maharasht ra , U.P., Rajasthan, J&K, West Bengal, Punjab and A. P.

b. Mat Weaving (Fibre) Industry

The development strategy of the KVIC envisages introduction of different improved tools for processing different fibres so as to make the product competitive in quality and finish. Some of the other objectives of the KVIC are identification of new sources of raw materials and development of improved methods of extraction of fibresi The scope for introduction of power loom in kora mat weaving for increasing productivity and wages was explored. A study was also conducted for manufacturing of design mats out of kora grass for improving matrketability and value addition. Prominent states under this industry are U.P., Maharashtra, Rajasthan, Punjab and West Bengal.

4. Assistance provided by Coir Bdard

It is ves ted with the responsibi l i ty of promot ing growth and development of the coir indus t r ies , promotion of exports and expansion of domestic market through publicity. The development programmes run by the Board include assistance for participation in exhibition, coir industry awards, Mahila Coir Yojana, strengthening

94

nat ional level t ra in ing ins t i tu t ions , model coir villages, group i n s u r a n c e s c h e m e s for a r t i s a n s , f inancial a s s i s t a n c e for modernisation and welfare measures.

5. Rural Employment Generation Programmes (REGP)

For generation of additional emplo5mient by millions, the Khadi and Village Industries Commission (KVIC) is entrusted with monitoring this massive programme. In 1995-96 district special employment programme and 125 block development programmes were merged under REGP. Till 31^' March, 2001 one milhon jobs have been created in the KVIC sector under the revised target of 1.5 million jobs by the end of the 9"" Plan period. Apart from this, REGP also conducts other programmes as enumerated below:

National Project on Village Industries

(a) Handmade Paper Industry (HMP) : Under this scheme it is proposed to set up 460 new HMP units providing employment to 40,000 persons.

(b) Leather Industry: This programme envisages setting up of 200 pro jec ts all over the coun t ry . Four projec ts have been sanctioned and implemented, one each at Kalyani (WB), Ambala (Haryana), Jalandhar (Punjab) and Agra (U.P.).

(c) Beekeeping Industry: This programme was supposed to generate employment of around 50,000 persons by the end of the 9"" Five Year PlEin by implementing 99 national projects all over the country.

6. Package for Tiny Sector

Government has taken various steps for the promotion of the tiny enterprises. These steps include earmarking of 60 per cent of credit flowing to the SSI sector under priority sector lending programme of bcinks for tiny units (40 per cent for tiny units having investment in plant and machinery up to Rs. 5 lakhs and 20 per cent share to units having investment in plant and machinery between Rs. 5 lakhs to Rs. 25 lakhs).

Tiny enterprises, a large number of which are unregistered units, have been made eligible for same ra te of excise exemption as available to the registered units since 1994-95. The excise exemption

95

limit for SSIs including tiny units has been enhanced from Rs. 30 lakhs to Rs. 50 lakhs. This will give inducement to the tiny units for increasing their production.

The Government has decided to adopt additional measures for the promotion of Tiny Enterprises by earmarking facilities for Tiny Units under the Integrated Infrastructural Development (IID) Scheme. The NSIC (National Small Industrial Corporation) would e£irmark 40 per cent of the assistance to the tiny units under the various schemes viz. supply of machinery on hire purchase, marketing support, technology assistance, training facilities etc. In order to ensure flow of credit to tiny units, a consistent and higher flow of credit to tiny units, the SIDBI will endeavour that up to 60 per cent of its refinance flows to the tiny sector.

m. Capacity Utilisation and Viability of Agro-Processing Units

In secondary data we no longer get information on capacity utilisation. One way to approach it could be for how many months* village agro-industries operate. As we have seen earlier in table 2.6 in chapter II that unlike OAME and NDME, a substantial portion around 39 per cent of DME operate only seasonally. However, looking at detailed table only for DME one can observe that it affects substantially three two-digit level industry groups (see table 5.14)

Table 5.14 : Proportion of Directory Manufacturing Enterprises (DME) Operating over Different Months

Sr.No. Number of Months Operated Total

1 Industiy (NIC 1998 code) 1-3 4-6 7-9 10-12

Total

2 Cotton ginning etc. (0145) 52.91 45.25 0.00 1.85 100

3 Food products (15) 46.95 25.61 4.53 22.91 100

4 Tobacco products (16) 37.16 36.95 1.36 24.53 100

5 Textile products (17) 1.20 2.92 4.09 91.79 100

6 Wearing apparel (18) 1.08 0.86 19.27 78.79 100

7 Teinning & leather products (19) 0.00 0.00 2.66 97.34 100

8 Wood & wood products (20) 1.97 6.89 4.19 86.95 100

9 Paper & paper products (21) 2.17 2.98 0.23 94.62 100

namely cotton ginning etc.. food products and tobacco products. In the two major industry groups food products and tobacco products, more that three-fourth of all enterprises work for less than seven

96

months. Even with working for so few months, food products account for 40 per cent of total gross vedue added. But, hardly 14 per cent of them complain about shortage of raw materials. This signifies that they are constrained by seasonality of food raw materials and their plants remain unutilised for greater peirt of the year. The way out is processing multiple food raw materials across different seasons and it is not at all happening for largest size group of village level food processing iildustiy.

IV Scope of Marketing Framework

The agro-industiy contains different size groups of enterprises. The market promotion structtire cannot be same for all size classes. The smallest and most numerous group of own account enterprises using only family labour were found in chapter IV to a large extent involved in putting out system. But within OAME this practice is dominant in three industry groups namely tobacco products, textile products and wearing apparels as can be observed from large share of other receipts in total receipts. The share of other receipts in wearing apparel is also veiy high in NDME and DME size classes as weU (Table 5.15).

Therefore, wearing apparel activity in rural areas seems to be dominated by tailoring activity and to a certain extent sub-contractual for larger size class of DME. In tobacco products and textile products in case of OAME for the enlargement market size, the middlemen/contractor needs to be bypassed. For that two steps are required. First, provision of timely credit to purchase raw materials and other inputs and second, building up of local market information network where products are sold. For providing timely credit, formation of independent self-help groups for OAME producing similar products need to be encouraged. Alternatively, primary credit societies can be rejuvenated for this purpose. For building local market information network, the purchasers and producers meet need to be encouraged through building up rural and nearly town agro-produce market where tiny producers can get idea about demand patterns and scope of market of their products. In this regard, existing co-operative marketing agencies can play active role. Such grassroots levels formation can be linked up to district, state and national level. Once market demands are ascertained then it is possible to tackle issues like upgradation of« process technology, quality of tools utilised, packaging of products etc. Government organisations like NSIC can make important contribution in this regard.

97

Table 5.15 : Extent of Putting out System and Contract Work and Agencies purchasing Output of Selected Agro-Industries

Industry/ Enterprise (NIC 1998 code)

Share of other receipts in total receipts (in %) {used as proxy

for extent of putting out

system)

Proportion of Enterprises under

contract (%)

Distribution of Agency for Sale of Final Product

Industry/ Enterprise (NIC 1998 code)

Share of other receipts in total receipts (in %) {used as proxy

for extent of putting out

system)

Proportion of Enterprises under

contract (%)

Private Enterprise

Middleman/

contractor

Private Individual

OAME

Tobacco products (161

85.92 93.00 14.00 80.30 2.60

Textile Products (17)

60.63 11.70 34.50 30.70 24.80

Wearing Apparels (18)

95.13 50.04 3.30 2.10 92.80

NDME

Food Products (15)

22.50 3.90 37.90 1.70 58.20


48.93 62.60 56.10 21.70 12.80


86.05 9.90 6.70 0.40 91.70

DME

Food Products (15)

3.09 3.20 69.40 5.80 19.60


34.77 62.20 71.00 19.40 4.90


62.68 23.10 33.30 0.70 65.60

Note : The share of these enterprises In above industry groups in total agro-industry in 52.86 percent in Gross Value Added.

For larger size enterprises like NDME and DME two aspects need to be tackled. First, one has to develop ways and means to increase the share of their products in government procurement particularly for DMEs s ince t he se en t e rp r i s e s have a l ready made small breakthrough in this regard. In textile products particularly, both NDME and DME are already substantially involved in subcontracting activity, this activity needs to be developed further by building up rural entrepreneurs association and make them interact vigorously with representatives of large establishments to further develop this

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synergy. This is particularly important in the era of removal of all quotas in the international trade.

For other major industry group food products, for size groups of NDME and DME, the share of other receipts in total receipts and proportion of enterprises are comparatively low compared to some other industry groups. But in the food products, the proportion sold to private enterprises are substantially higher particularly for DME. This linkage needs to be developed further. But the problem, as we have ment ioned earlier, is t ha t DMEs in the food p roduc t s is constraint by capacity utilisation as two-third of them operate for less than seven months in a year.

CHAPTER VI

SUMMARY, CONCLUSIONS AND BROAD POLICY SUGGESTIONS

We first present summary and conclusions of different chapters . Lastly, on the basis of all these aspects, we present the broad policy suggestions.

Summary of Chapters

The rural manufacturing employment has not seen subs tan t ia l increase in the last two decades. Consequently, the share of rural manufacturing emplo3niient in rural employment has Increased, but that in rural non-farm employment has declined. In net domestic p roduc t (NDP), the sha re of ru ra l manufac tu r ing h a s shown substantial fall in the 1980s but remained stable in the 90s. The reason is that the Gross Value Added (GVA) originating from rural unregistered manufacturing has not gone up in real terms in the 80s whereas in the 90s it has gone up by almost 50 per cent.

The manufacturing sector occupies an important position in the ru ra l informal non-farm sector, employing 45 per cent of i ts workforce. Still, as manufacturing sector is relatively more labour-intensive than other non-farm sectors, the value added per worker is lower as compared to the other non-farm sectors. However, in the informal sector, the manufacturing establishments are evenly placed with t he o ther n o n - a g r i c u l t u r a l e s t a b l i s h m e n t s w h e r e a s manufacturing own account enterprises (OAE) is worse placed as compared to the other non-agricultural OAEs.

On comparing economic census of 1998 and the informal sector survey of 1999-2000, one can find that the share of manufacturing in the non-farm informal sector in terms of number of enterprises is much larger than that of the unorganised sector in the economic census of 1998. It goes to reflect that enterprises belonging to the non-manufacturing non-farm sector are more likely to be registered. The manufacturing sector enterprises register only when they reach some threshold size.

More than nine-tenths of the agro-industries is operating all through out the year. But one-third of DME is seasonal . In the case of registration, the picture is just the opposite. Hardly 5 per cent of the

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agro-industry is registered. It is almost negligible in the case of OAME but gets progressively better as one moves up to NDME and DME.

The growth of village level agro-industry in terms of number of units, emplo5rment and value added went downhill from 1984-85 to 1989-90 and further to 1994-95. Only in 2000-01, it showed improvement or s tab i l i ty in all c h a r a c t e r i s t i c s namely n u m b e r of u n i t s , employment and gross value added. In relation to the non-agro industry over the years, the share of agro-industry in terms of number of uni ts show marginal decline, in terms of employment moderate decline and in terms of gross value added substant ial decline over 12 per cent in last two decades. However, since the performance of the village level agro-industries is far better in the post-liberalisation period compared to the pre-liberalisation period (1984-85 to 1994-95), its sha re vis-a-vis non-agro indus t r i es improved in n u m b e r of u n i t s and employment and did not deteriorate in value added. But the productivity gap between the agro and non-agro industries is going up over the years even in the post l iberalisat ion period. The major reason of this increased disparity lies in substantially more fixed asset per worker in the non-agro^rocessing as compared to the agro-processing. Even then the agro-industry still dominates rural manufacturing in terms of aU mjajor characteristics.

The composition and size distribution of village level agro-industries are analysed only for the year 2000-01. New industrial classification for 2000-01 is substantially different from the earlier industrial classif icat ion on which êarlier r o u n d s of unorgan i sed sector manufacturing data were collected. Further, new industries were added to manufacturing from agriculture and service sector. To compare over the years we have to exclude these sub-sectors. In order to avoid it, we confined ourselves to the year 2000-01.

Out of eight industry groups at the two digit level, only five have substantial presence in the agro-industry. The major two industry groups are food p roduc t s and wood p roduc t s . Food p roduc t s dominates over all other industry groups in number of units and employment characteristic in NDME and DME but wood products is marginally more important in OAME than food products in both these characteristics. It is only by dint of its presence in OAME that wood products turns out to be the second most important industry in the overall agro-industry at the two digit level. But in the case of gross value added, the picture is somewhat different. Food products

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dominates over all other industry groups in all size groups. Only in OAME, the share of wood products in the whole agro-industry in this characteristic is one-fourth.

The total fixed asset of village level agro-industry is a round 23 thousand crores. Out of which OAME account for Rs. 16 thousand crores. At the two digit level, food products dominates in th is characteristic in all size groups — its share in the village level agro-industry is overwhelming at 45 per cent.

Lx)oking at the size distribution, it can be seen that the domination of the smallest size group OAME is overwhelming in the village agro-industries. The share of OAME in the agro-industry is nine-tenths in number of units, fifth-sixths in employment characteristic, three-fourths in gross valued added and seven-tenths in fixed assets. At the two digit level also, the scenario is similar except In two small agro-industry groups, namely cotton ginning and paper products where the DME has substantial presence in gross value added and fixed asset characteristic.

Before examining backward production linkage It will be worthwhile to examine wha t percentage of agr icu l tura l i npu t is ac tua l ly processed by the agro-industry. This is seen in two steps. First, the proportion of agricultural Input that goes for intermediate use . Secondly, the proportion of this intermediate use that goes for agro-p rocess ing . For t ha t , we divided the whole of a g r i c u l t u r a l commodities in the four major groups. These are plantation crop, commercial crop, food crop and agricultural & allied sector. In both plantation and commercial crop categories, both the proportion of intermediate use that goes for agro-processing are very high. More than 90 per cent of output of these two sectors gets processed in agro-industry. Only in the case of coffee and coconut in plantation crop and sugarcane in commercial crop category, a subs tan t ia l proportion of output of these two crops gets directly consumed in homemade coffee consumption and in sugarcane juice respectively. Over the years (I.e. in between 1983-84 and 1993-94), the direct consumption of coffee and jute shows substantlEil increase and that of tobacco shows substantial decline.

By contrast , in food crop the proportion of output available for intermediate use and proportion of intermediate use that goes into ag ro -p rocess ing is m u c h lower t h a n in t h a t p l a n t a t i o n and commercial crops. However, the proportion of intermediate use that goes for agro-processing shows improvement in between 1983-84 and 1993-94 except for other crop category. On the whole, it shows that

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raw material supply constraints, through its fluctuations in different years , is not going to const ra int ou tpu t of agro- industry tha t processes the output of these agricultural commodities since these are mostly consumed in the non-processed form. The agricultural & allied sector in this regard falls somewhat in between and the variation within this sector in terms of both proportion of output that goes for Intermediate use and the proportion of intermediate use that goes for agro-processing is quite varied. In both fishing and milk & milk products category, the proportion of intermediate use that goes for agro-processing shows substant ial rise in between 1983-84 and 1993-94.

There is another dimension that weakens growth linkage between agricul ture and agro-industry. The secondary processing agro-industry tha t processes already primary processed agricultural commodities is relatively Insulated from the annual fluctuations in the output of agricultural commodities. In the same token, the capacity of secondary processor agro-industry to stimulate growth Impulse in agricultural sector also gets Umited. On the basis of input use patterns we categorised agro-industry into four namely (1) mainly agricultural Input purchasing; (11) mainly agro-input purchasing; (Hi) primarily agricultural and secondarily agro-industry input purchasing and (iv) primarily agro-industry and secondarily agricultural input purchasing. The number of agro-Industries belonging to category ii (mainly agro- input purchasing) h a s fallen by two and the net addition or two agro-industries have occurred in the category 1 (mainly agricultural input purchasing) in between 1983-84 and 1993-94.

The direct backward production linkage of village level agro-industry in the year 1993-94 is quite higher at 0.5813. At the two digit level, out of nine agro-industr ies , only in three agro-industr ies food products, wool textiles and leather & leather products, the backward production linkage in higher than that of the agro-industry level. When we classify these nine agro-industries into input use pattern, we find that only three agro-industries, viz., food products, beverage & tobacco p r o d u c t s and wood & wood p roduc t s are pr imary processing and the rest six agro-industries are secondary processing. So, at the village level, where agricultural production takes place, the village agro-industries at the two digit level are largely secondary processing, using agro-industry input as Its main raw material. The backward linkages of the secondary processing agro-industries are much higher than that of the primary processor except for food products.

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At the all India level, the classification of agro-industry into primary and secondary processing is almost similar except for cotton textile and jute textile which are largely secondary processing at the village level are primary processing at the all India level. It goes to show that for these two industry groups primary processing is undertaken outside the village level agro-industry. In most of the primary processing and secondary processing agro-industry, the shares of agricultural input and agro-industry inputs respectively are higher than that of the aU-India level agro-industry. It goes to show that as compared to the all India level, village level agro-industries are either at the beginning of primary processing or at the fag end of the secondary processing.

In the case of direct forward production linkage, when seen only within the manufacturing sector — the overall forward linkage of village level agro-industry is quite small only at 0.122 thereby showing that only 12 per cent of agro-industry's output gets used within the village level manufacturing. But most of it gets used within the agro-industry sector itself. It is even true at the two-digit level agro-industry. At the two digit level, only in the case of cotton textile and wool textiles forward linkage is substantially higher — even here most of it is used within the two-digit level industry group itself.

At the all India level, the forward linkage of agro- indus t ry is somewhat higher . 'But even here most of its output at the agro-industry £ind the two digit level within the manufacturing sector gets used within the same industry showing overwhelming domination of intra-industry trade. But, in all India, consumption of agro-products in non-agro industry is substantially higher. The major reason is that in three industries at the two digit level namely jute textiles, paper products and wood products around one-fifth of their output gets used in the non-agro Industries. The obvious explanation is that ou tpu t of these agro- industr ies is used in large proport ion in packaging of mostly branded products. The outlet is not available within the periphery of the village level manufacturing Industries.

Across the size of enterprise, the GVA per worker is lower in agro enterprises than in the non-agro enterprises. The difference of GVA per worker between the agro and non-agro is most prominent in the case of directory enterprises followed by the non-directory and own account en terpr i ses . The reason is lower levels of infusion of technology in the agro enterprises in comparison with that in the non-agro enterprises which is reflected by lower value of fixed asset

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per enterprise and per worker in the agro enterprises. Within the agro enterprises the industry groups with higher value of fixed asset per enterprise and per worker report higher productivity. In general, Tanning and dressing of leather & leather products' shows highest value of both GVA and profit per worker. However, among the directory establishments, 'Paper and paper products ' shows the highest productivity per worker in terms of GVA as well as profit.

The ratio of GVA to fixed assets is lower in the agro enterprises mainly because of lower value of the GVA in the agro enterprises. This also indirectly hints that the use of capital per unit of GVA is higher in the agro enterprises in comparison with their non-agro counterpar t , leading to a higher capital ou tpu t ratio in these enterprises. However, it is amply clear that the higher capital output ratio in the agro enterprises arises not because of higher use of capital bu t mainly because of lower value of GVA in comparison with that in the non-agro enterprises.

Within the agro enterprises, the value of fixed assets per enterprise as well as per worker is the highest in Cotton ginning etc. followed by Paper and paper products and the lowest in Tobacco products followed by Wood and wood products.

In the agro-enterprises, the proportion of manufacturing expenses to total expense is significantly higher t h a n tha t in the non-agro enterprises. However, in comparison with the non-agro enterprises both per worker manufacturing expenses and total expenses are lower in the agro enterprises in the NDME category. Manufacturing expenses per worker in the agro enterprises is significantly higher than that in the non-agro in the GAME and DME categories.

Because of the low rate of GVA and profit and high share of manufacturing expenses to total expenses, the agro enterprises are depending more on the 'putting Out' system in order to earn profit. This is reflected in the high proportion of 'other' receipts (low proportion of manufacturing receipts) in the total receipts of the enterpr ises . This is part icularly more dominant in the case of industry groups which have low capital base and low infusion of technology.

Overall the access to credit and credit institutions is very poor in the case of agro enterprises. Moreover, the role of formal financial ins t i tut ions has been absolutely negligible at least in terms of coverage of number of enterprises. It is ironical that even within the core manufactur ing sector more than half the number of agro

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en te rp r i se s is covered by informal sector for t he pu rpose of financing. In terms of size of loans also the situation is very poor. The average size of loan outstanding for the agro enterprises works out to be merely Rs. 1,800 as against more than Rs. 8,000 in the non-agro enterprises. In tobacco, the average size of loan outstanding is as low as Rs. 200 per enterprise.

It is amply clear that poor access to credit particularly from formal financial institutions has constrained the agro enterprises to invest in capital and technology and expand viably. This has led to low r a t e of r e t u r n bo th in t e r m s of GVA and prof i ts . The agro enterprises, hence, are taking the help of putting out system in order to generate respectable earnings. Overall, it seems that the financial viability of agro-enterprises is marred by low level of technology use, seasonal nature of operation and poor access to formal credit institutions. In fact, there exists an interrelationship between these three and agro-enterprises are caught in the vicious circle of low capi ta l b a s e - low credi t - low s u r p l u s - h igh dependence on putting out system - low manufacturing activities -low credit - low capital base. In order to enhance the viability of agro enterprises there is urgent need to provide a big push of capital intervention particularly in the industry groups of food processing, tobacco products, leather & tanning and wood products.

Broad Policy Suggestions

1. Backward production linkage of agro-industry is much stronger than its forward production linkage. But, as compared to all India, the forward production linkages of village level agro-industries are weaker. The whole of agricultural production takes place in rural areas but the village level agro-industry is largely involved in secondary processing. At all India level, however, the agro-industry is mainly involved in primary processing. Therefore, the output of viUage level agro-industries is more constrained by marketing since a smaller proportion of their output gets used as input in other manufacturing activities. This marketing problem has led to widespread prevalence of business service activities part icularly among the smallest size group of own account en te rpr i ses which is akin to the pu t t ing out sys tem. This phenomenon is leading to a vicious circle of low productivity, low earnings and low level of technology. Marketing infrastructure needs to be urgently provided to them—in the form of rura l mandies, ma rke t ing coopera t ives , larger p u r c h a s e s by governments, strengthening linkage with larger sized enterprises,

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etc. However, marketing framework will vary across industry groups and size classes.

At the village level, the agro-industries in comparison to the non-agro industries have lower level of gross value added and they have higher capital output ratio. This is indicative of the low level of efQciency of the village level agro-industries. It necessitates the importance of absorbing more efficient technologies in agro-industries and reducing the gap even with non-agro industries.

The access to credit is very low in the case of village level agro-industries. Even the role of organised financial institutions has been virtually negligible in terms of coverage. It is ironical that even in this core manufacturing activity, more than half of village level agro-industries access credit from Informal sources. Although the average size of loan outstanding is higher in the case of financing by the organised financial institutions, the average loan outstanding is abysmally low in the agro-industries.

It is c lear t h a t the access to credi t pa r t i cu la r ly from the organised financial institutions has constrained agro-industry to invest in fixed capital and new technologies and thus expand viably. The vicious circle of low credit - low surp lus - high dependence on putting out system - low level of manufacturing activities needs to be broken by large-scale infusion of credit from the formal sector.

The village level agro-industry does not come within the purview of any single Ministry. Consequently, it comes under the purview of multiple registration authorities. Because of this problem, only a fraction of the village level agro-industries are registered. An overwhelming proport ion of the registered en te rp r i ses are registered with the village Panchayat. To infuse technology and credit in agro-industry, it is required to bring them under single registration authority and start a massive campaign to register village level agro-industries.

In spite of the initiation of several government programmes, lack of infrastructural facilities hinders the growth of agro-industries. These include electricity connection, power cut, availability of raw materials, transportation, etc. Infrastructural facilities need to be upgraded substantially for economic viability of these enterprises through widespread development of infrastructure.

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The problem of seasonality of larger size enterprises in food product and tobacco product industries needs to be tackled by making these enterprises suitable for processing multiple food items over different seasons.

We have already seen high prevalence of contract work in the agro-industries. To make such arrangement non-exploitative, some method needs to be evolved regarding the appropriate pricing of the p roduc ts sold by these en terpr i ses to larger establishments or merchants.

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Documents

STATUS AND POTENTIALS OF VILLAGE AGRO-PROCESSING UNITS / INDUSTRIES