MACRO AND FARM LEVEL INVESTMENT IN INDIA ... fact that the growth rate in the farming economy...

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MACRO AND FARM LEVEL INVESTMENT IN INDIA: TRENDS, DETERMINANTS AND POLICIES

S.Mahendra Dev

Vice Chancellor and DirectorIndira Gandhi Institute of Development

Research (IGIDR), Mumbai, India

SEPTEMBER, 2011

Study prepared for FAO (Rome)

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Macro and Farm Level Investment in India: Trends, Determinants and Policies

S. Mahendra Dev"Within the agricultural sector, the degree of progress attained largely depends upon how the farmers deploy the additional incomes generated from year to year from their farm activities. This stems from the fact that the growth rate in the farming economy largely depends on the stock of capital built by the farming community and the ploughing back of such stocks in the form of savings for further improvement of farm activity. If these increments are spent on household expenditure, without building up the necessary infrastructure, the future economic development of the nation will be hampered" (Report of the High Level Committee on Estimation of Savings and Investment, Chaired by C.Rangarajan, GOI, 2009)

SECTION 1INTRODUCTION

The positive association between capital formation and agricultural growth is well known.

Higher capital-labour ratio increases land and labour productivities in agriculture which in turn

raise incomes of the farmers and reduction in poverty and hunger. Two-thirds of investment in

agriculture is generated by private sector particularly on farm investment. Some evidence also

shows that public and private investments are not-substitutable entities. For example, public

investment in roads and infrastructure can not be created by farmers. Similarly corporate

investment is mostly in post-harvest activities like processing and high value chains. Therefore,

there is a need for capital formation by farmers from their own savings. The evidence at the

aggregate level shows that this component has been stagnant or declining. Therefore, one of the

important topic for analysis could be how to maximize savings and on-farm investment by

farmers. This study has analysed the following: (a) What are the trends and composition of

savings and investments in Indian economy? (b) What is the evidence on savings and on-farm

investment of farmers? (c) How does price policy influence farm profitability? (d) What are the

determinants of farm investments by the farmers?; What are the policies needed for maximizing

on-farm investments?

The sources of data base for this study are National Account Statistics, National Sample Surveys,

All India Debt and Investment Surveys, secondary data and cost of cultivation surveys.

The paper is organized as follows. Section 2 examines trends in household savings and

investments in the whole economy while section 3 deals with indebtedness and credit for

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agricultural households. Section 4 examines price policy and farm profitability. Section 5

analyses the Trends in private investment in agriculture with the data from National Accounts

Statistics and All India debt and investment surveys. Section 6 examines growth and composition

of the capital assets using the farm level data from the cost of cultivation surveys. Using the

same farm level data, section 7 examines the determinants of output, labour productivity and

capital formation for pooled data as well for groups of farmers. The last section deals with

conclusions and policies needed for maximizing on farm investment in Indian agriculture.

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SECTION 2

TRENDS IN HOUSEHOLD SAVINGS ANDINVESTMENTS IN THE ECONOMY

Here we look at first at the level of the economy on house hold savings and investments at the

macro level as a background to the analysis on agricultural on –farm savings and investments.

The trends in savings and investments for the economy will be at all India level. It is known that

in India, investment is almost completely financed by domestic savings as foreign sources are

limited.

2.1. Trends in SavingsThe trends in gross domestic savings show that it increased from 8.6% in 1950-51 to 36.9% in

2007-08 before declining to 32 to 34% in 2008-09 and 2009-10 respectively (Table 2.1).

Household sector plays an important role in savings as compared to private corporate sector and

public sector. As per cent of GDP, household sector savings increased from 5.7% in 1950-51 to

12.9% in 1980-81 to 18.4% in 1990-91 and to 24.1 % in 2003-04.

The growth rate of household savings was around 10% per annum in the 1980s. It declined to

7.5% in the 1990s but again increased to 8.6% in 2000s (see Table 2.2 and Fig 2.1). It is

important to note that household savings constitute around two-thirds of gross domestic savings

in the country (See Table 2.3 and Fig 2.2)

Table 2.1. Domestic Savings by Institutions as per cent of GDP: 1950-51 to 2009-10In 199-200 prices

1950-51 5.7 0.9 2.0 8.61951-52 5.1 1.3 2.7 9.01952-53 5.7 0.6 1.7 8.01953-54 5.4 0.8 1.5 7.61954-55 6.2 1.1 1.8 9.11955-56 9.0 1.2 2.1 12.31956-57 8.4 1.2 2.3 11.91957-58 6.8 0.9 2.3 10.01958-59 6.2 0.9 2.0 9.11959-60 7.6 1.2 2.1 10.81960-61 6.5 1.6 3.1 11.21961-62 6.2 1.7 3.3 11.21962-63 7.0 1.7 3.5 12.31963-64 6.3 1.7 3.8 11.91964-65 6.3 1.5 3.8 11.61965-66 8.6 1.4 3.6 13.71966-67 9.5 1.3 2.8 13.61967-68 8.1 1.1 2.4 11.61968-69 7.9 1.1 2.8 11.8

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1969-70 9.7 1.3 3.0 14.01970-71 9.5 1.5 3.3 14.21971-72 9.9 1.6 3.2 14.71972-73 9.6 1.5 3.1 14.31973-74 11.4 1.6 3.4 16.41974-75 9.6 1.9 4.0 15.71975-76 10.9 1.3 4.7 16.91976-77 12.4 1.3 5.4 19.11977-78 13.2 1.4 4.8 19.51978-79 14.6 1.5 5.1 21.21979-80 13.0 2.0 4.9 19.81980-81 12.9 1.6 4.0 18.51981-82 11.5 1.5 5.1 18.11982-83 11.1 1.6 5.0 17.71983-84 11.7 1.5 3.9 17.11984-85 13.1 1.6 3.5 18.21985-86 13.1 1.9 3.9 19.01986-87 13.2 1.7 3.5 18.41987-88 15.6 1.7 2.9 20.21988-89 15.8 2.0 2.8 20.51989-90 17.0 2.4 2.4 21.81990-91 18.4 2.7 1.8 22.81991-92 15.8 3.1 2.6 21.51992-93 16.4 2.7 2.2 21.21993-94 17.3 3.4 1.2 21.91994-95 18.6 3.5 2.3 24.41995-96 16.9 5.0 2.6 24.41996-97 16.0 4.5 2.2 22.71997-98 17.7 4.3 1.8 23.81998-99 18.8 3.9 -0.5 22.31999-2000 21.1 4.5 -0.8 24.82000-01 21.6 3.9 -1.8 23.72001-02 22.1 3.4 -2.0 23.52002-03 22.9 4.0 -0.6 26.32003-04 24.1 4.6 1.1 29.8

In 2004-05 prices2004-05 23.6 6.6 2.3 32.42005-06 23.5 7.5 2.4 33.52006-07 23.2 7.9 3.6 34.62007-08 22.5 9.4 5.0 36.92008-09p 23.8 7.9 0.5 32.22009-10q 23.5 8.1 2.1 33.7Source: Economic Survey 2010-11, Government of India, 2011. P: provisional estimates; q= quick estimates

Table 2.2 Growth Rates of Household Savings: 1980-81 to 2008-09Periods Growth Rates (%)1980-81 to 1989-90 9.71990-91 to 1999-00 7.52000-01 to 2008-09 8.6

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Figure 2.1: Decadal growth rate of household savings

Table 2.3: Share of household savings in gross domestic savings

Years

Share of household savings in gross domestic savings (in %)

1980-81 69.66

1981-82 63.63

1982-83 62.83

1983-84 68.58

1984-85 71.92

1985-86 69.19

1986-87 71.77

1987-88 77.36

1988-89 76.76

1989-90 78.01

1990-91 80.60

1991-92 73.35

1992-93 77.23

1993-94 78.73

1994-95 76.29

1995-96 69.08

1996-97 70.58

1997-98 74.36

1998-99 84.61

1999-00 85.19

2000-01 91.15

2001-02 94.26

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2002-03 87.83

2003-04 81.70

2004-05 76.53

2005-06 70.89

2006-07 67.44

2007-08 62.16

2008-09 73.47

2009-10 69.59

Figure 2.2: Composition of total savings

Household savings has two components viz., physical savings and financial savings. Both

components as per cent of GDP have increased over time. The share of financial savings in

the total household savings increased significantly from 25.0 per cent in the 1950s to around

47.0 percent in the five years ending 2006-07 (GOI, 2009) (Table 2.4). Physical assets

increased particularly since the early 2000s because of demand for construction particularly

housing.

Table 2.4. Components of Household Savings as per cent of GDP1950s 1960s 1970s 1980s 1991- 1997- 2003- 2000- 2007- 2008-

All the figures in Rs. Crore (at 1999-00 base)

Source: Handbook of Statistics, RBI

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92 to 1996-97

98 to 2002-03

04 to 2006-07

2007 08 09

HH saings

6.6 7.6 11.4 13.5 16.8 20.8 23.8 23.2 22.6 22.8

(a) Physical savings

4.7 4.9 6.9 6.8 6.8 10.5 12.7 12.3 11.5 12.2

(b) Financial Savings

1.9 2.7 4.5 6.7 10.0 10.3 11.1 10.8 11.2 10.4

Savings in the Farm SectorIt is known that majority of population in India depend on agriculture sector, therefore,

savings in this sector are important for investment. Inspite of its importance, we do not have

estimation of savings in farm sector. There are constraints of estimation of farm sector

savings. One of the problems is that most of the activities in agriculture fall under

unorganized sector. It is difficult to find the regular account of income, expenditure and

savings of farmers. They also have multiple activities and different sources of income. Also,

substantial portion of area unbanked and farm sector savings are uncertain, volatile and

difficult to quantify.

The National Sample Survey Organisation (NSSO) undertook a comprehensive survey to

assess the situation of farmers in the country during 2003 at the request of the Union Ministry

of Agriculture. The relevant areas covered by the survey were education level of farmer

households, the level of living measured by consumer expenditure, income and productive

assets, indebtedness, farming practices and preferences, resource availability, awareness of

technology and access to modern technology in agriculture1. In the survey, a farmer

household is defined as one in which at least one family member was farmer. Agricultural

activity was taken to include cultivation of field and horticultural crops, growing of trees or

plants such as rubber , cashew, coconut, pepper, coffee, tea, etc; animal husbandry, fishery,

bee-keeping, vermiculture, sericulture, etc.

Based on the data from the above survey of NSSO, GOI (2009) tried to estimates of savings

in agriculture. The survey provides income, expenditures and investments of farm

1 The five reports of the Situation Assessment Survey of the farmers released by the NSSO are: Consumer Expenditure of Farmer Households (495), Some Aspects of Farming (496), Income, Expenditure and Productive Assets of Farmer Households (497), Indebtedness of farming households (498), and Access to Modern Technology for Farming (499). See Bhalla (2006a) for an analysis on state of farming community based on these reports.

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households. The share of wages in average monthly income was 39% at all India level but it

was as high as 62% in Rajasthan, 53-54% in Orissa and Tamil Nadu, 50% in Kerala and 43%

in West Bengal (Table 2.5). In rest of the States, income from cultivation exceeded wage

income in farm households.

Table 2.5. Income, Expenditure, Loan Outstanding per Farmer households: All India, 2002-03States Average Monthly Income (Rs.)per farmer

households fromAverage Monthly Consumption expenditure (Rs.)

Average amount of cash loan per household (Rs.)

Andhra Pradesh 643 743 93 155 1634 2386 2325 16154Assam 973 1792 141 255 3161 2714 2704 641Bihar 497 846 265 202 1810 2459 2407 3336Chattisgarh 709 811 -3 101 1618 2045 2084 4833Gujarat 925 1164 455 140 2684 3127 3255 12958Haryana 1268 1494 -236 356 2882 4414 4217 17340Jammu&Kashmir 2060 2426 382 620 5488 4109 4492 1198Jharkhand 924 852 86 207 2069 1897 2110 1021Karnataka 1051 1266 131 168 2616 2608 2724 13422Kerala 2013 1120 154 717 4004 4250 4316 27641Madhya Pradesh 560 996 -227 101 1430 2339 2457 12246Maharashtra 799 1263 144 257 2463 2689 2803 14268Orissa 573 336 16 137 1062 1697 1831 3976Punjab 1462 2822 236 440 4960 4840 4696 25211Rajasthan 931 359 5 203 1498 3288 3078 13261Tamil Nadu 1105 659 110 198 2072 2506 2436 14823Uttar Pradesh 559 836 53 185 1633 2899 2952 5363West Bengal 887 737 77 378 2079 2668 2690 3820All India 819 969 91 236 2115 2770 2770 9261

Situation Assessment Survey (SAS) of Farmers (2003), NSSO, 2005

Based on available data on the income and consumption expenditure of farm households, GOI

(2009) attempts to estimate the savings of farm sector during 2002-03. The Report of the

High Level Committee on Estimation of Saving and Investment indicates that the average

monthly savings of farmer household have turned out to be negative (Rs.655) if we use

Keynesian concept viz., S=Y-C. where S is savings, Y is income and C is consumption. It

indicates that there are dis-savings in the farm sector (Table 2.6). The negative annual savings

of all cultivators are estimated to be Rs.69,348 crores. The ratio of farm sector savings to

overall GDP is estimated at -2.8 per cent for the year 2002-03. Using the cash loans as

proportion of GDP, the indebtedness of cultivator households from AIDIS is estimated to be

3.3% of GDP in 2002-03. This estimate is closer to dis-savings ratio of the cultivator

households. In other words, the gap between income and consumption expenditure of

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households. The overall gross capital formation as proportion of GDP in the year 2002-03

was 2.1% (Table 2.6). This analysis shows that there is a wide gap between investment and

savings in the farm sector. It also indicates that mobilisation of savings is important for

raising investment in farm sector.

Table 2.6. Estimation of Savings in the Farm Sector (2002-03)Item (Amount in Rs.)1 Sample households (number) 51,7702 Average monthly income per farmer households 2,1153 Average monthly consumption expenditure per farmer households 2,7704 Average savings per farmer household -6555 Average amount of cash loan per cultivator household 9261

(Amount in crores)6 Estimated no. total cultivator households (Number) 8,82,29,6007 Total monthly income of cultivator households 186618 Total monthly consumption expenditure of cultivating households 244409 Total monthly savings of all cultivator households -577910 Total annual savings of all cultivator households -6934811 Total amount of cash loan of all cultivator households 81709

(Amount in crores)12 Overall GDP at current market prices 245456113 Agriculture GDP 42552114 GCF in agriculture 52123

(in per cent)15 GCF in agriculture as a proportion of overall GDP 2.116 Agriculture savings as a proportion of overall GDP -2.817 GCF in agriculture as a proportion of agriculture GDP 12.218 Agriculture savings as a proportion of overall GDP -16.319 Total amount of cash loans as a proportion of overall GDP 3.320 Total amount of cash loans as a proportion of GDP in agriculture 19.2

Source: GOI, 2009; compiled based on data from Situation Assessment Survey (SAS) of Farmers and All India Debt and Investment Survey (2003), NSSO

Trends in Investment for the EconomyGross capital formation in India increased from 11.2 per cent of GDP in the 1950s to 36 to 38

per cent in the late 2000s. Household investment as per cent of GDP rose from 4.7% in the

1950s to 12.7% in 2003-07. (Table 2.7). Private corporate investment increased particularly in

the 2000s significantly. Savings-investment gaps given in Table 2.7 provide interesting

trends. This gap for household sector rose significantly from 1.9% in the 1950s to 11.1% in

2003-07. In other words, savings are much higher than investment for household sector. In the

case of private corporate sector and public sector, there are negative savings as investments

are higher than savings.

Table 2.7. Investments and Savings-Investment Gap

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1950s 1960s 1970s 1980s 1990s 1991-92 to1996-97

1997-98 to 2002-03

2003-04 to 2006-07

2000 to 2007

Gross capital formation

11.2 15.2 18.1 21.9 23.6 23.2 24.4 32.2 28.9

(a)Household investment

4.7 4.9 6.9 6.8 7.8 6.8 10.5 12.7 12.3

(b)Private corporate investment

1.9 2.9 2.6 4.5 7.4 7.7 6.6 11.2 8.8

(c)Public sector investment

4.6 7.3 8.6 10.6 8.4 8.7 6.9 7.1 6.9

Savings-investment Gap

-1.1 -2.0 -0.1 -1.8 -1.4 -1.2 -0.4 -0.2 0.1

(a)Household sector

1.9 2.7 4.5 6.7 8.9 10.0 10.3 11.1 10.9

(b)Private corporate sector

-0.9 -1.5 -1.0 -2.8 -3.6 -4.0 -2.6 -4.7 -3.5

(c) public sector -2.6 -4.1 -4.4 -6.9 -6.9 -6.5 -7.5 -4.9 -6.7Source: GOI (2009)The growth rate of household investment was 11% in 1980s and declined to 7.8% in the 1990s

before increasing to 8% in 2000s (Fig 2.3.). The composition shows that the share of household

sector in total gross capital formation showed fluctuations from 20% in 1982-83 to 50% in 2002-

03. During 2005-06 to 2008-09, the share declined and it was around 30 to 34% ( Table 2.8, Fig

2.4.).

Figure 2.3: Growth of household investment

Table 2.8: Share of household investment in gross capital formation

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share of household

investment in gross capital formation (in %)

1980-81 37.51

1981-82 26.51

1982-83 20.74

1983-84 29.58

1984-85 27.83

1985-86 27.88

1986-87 25.27

1987-88 36.95

1988-89 39.59

1989-90 38.70

1990-91 40.08

1991-92 28.77

1992-93 32.39

1993-94 29.82

1994-95 28.48

1995-96 30.07

1996-97 26.01

1997-98 33.57

1998-99 37.69

1999-00 40.41

2000-01 47.17

2001-02 46.58

2002-03 50.09

2003-04 47.52

2004-05 40.09

2005-06 34.41

2006-07 33.15

2007-08 30.52

2008-09 34.27

All the figures in Rs. Crore (at 1999-00 base)

Source: Handbook of Statistics, RBI

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Figure 2.4: Composition of gross capital formation

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SECTION 3

FINANCIAL INCLUSION AND INDEBTEDNESS

The nationalization of banks in 1969 and subsequent developments led to expansion of the

geographical and functional reach by commercial banks, regional rural banks (RRBs) and

cooperative credit institutions. Public policy aimed at ‘social’ and ‘development banking’ in the

form of meeting rural credit needs and reducing the role of informal sector credit. It may be

noted that despite the vast expansion, a large number of vulnerable groups remain excluded from

the opportunities and services provided by the financial sector. Such excluded groups include

small and marginal farmers, women, unorganized sector workers including artisans, the self

employed and the pensioners.

The Finance Minister indicated in the Budget 2006-07 that ‘out of the total number of cultivator

households only 27 per cent receive credit from formal sources and 22 per cent from informal

sources’ (p.11, GOI, 2006). In the Budget speech, he proposed to appoint a Committee on

Financial Inclusion. Based on this announcement, the Government of India has set up a

committee on Financial Inclusion under the Chairmanship of Dr. Rangarajan to suggest ways and

means to extend the reach of financial sector to excluded groups by minimizing the barriers to

access financial services. RBI and NABARD are also concerned about financial exclusion of

many households.

3.1. Definition of Financial InclusionFinancial inclusion can be defined as delivery of banking services at an affordable cost to

the vast sections of disadvantaged and low income groups.. In the case of credit, the proper

definition of financial exclusion includes households who are denied the credit in spite of their

demand. Although credit is the most important component, financial inclusion covers various

other financial services such as savings, insurance, payments and remittance facilities by the

formal financial system to those who tend to be excluded2.

2 More on the definition of financial inclusion see Thorat (2006)

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In the case of credit, many households are being exploited by the money lenders at very high

interest rates (50 to 60%) and, therefore, these households should not be included under financial

inclusion. Therefore, financial inclusion refers to households accessing institutional credit

including commercial banks, co-operative banks, RRBs, NABARD SHG-linkage and other self

help groups, credible micro finance institutions.

It is possible that in order to fulfill targets of financial inclusion, more bank accounts may be

opened in the formal system. However, opening bank account it self is not sufficient. Financial

inclusion also refers to more efforts towards covering small and marginal farmers and vulnerable

social groups. Broader definition of inclusion should also focus not only on credit but also on

increase in productivity and sustainability of farmers and other vulnerable groups.

3.2. Dimensions of Farmers’ Indebtedness and Extent of Financial Inclusion

Credit to farmer households is one of the important elements of financial inclusion. In order to

know the extent of credit inclusion, ideally we should have data on the households who are

denied credit in spite of demand. Since we do not have such readily available data, we use here

farmers’ indebtedness as proxy. According to the 59th round survey of NSSO (report no.498) we

have nearly 150 million rural households out of which around 90 million are farmer households.

Report no.498 of NSS on Indebtedness of farm households provides the following conclusions.

• At all-India level, estimated number of rural households was 147.90 million, of whom

60.4% were farmer households.

• Out of 89.35 million farmer households, 43.42 million (48.6%) were reported to be

indebted.

• Estimated prevalence of indebtedness among farmer households was highest in

Andhra Pradesh (82.0%), followed by Tamil Nadu (74.5%) and Punjab (65.4%).

• Estimated number of indebted farmer households was highest in Uttar Pradesh (6.9

million), followed by Andhra Pradesh (4.9 million) and Maharashtra (3.6 million).

• Going by principal source of income, 57% farmer households were cultivators.

Among them 48% were indebted.

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• Households with 1 hectare or less land accounted for 66% of all farmer households.

About 45% of them were indebted.

• More than 50% of indebted farmer households had taken loan for the purpose of

capital or current expenditure in farm business. Such loans accounted for 584 rupees

out of every 1000 rupees of outstanding loan.

• Marriages and ceremonies accounted for 111 rupees per 1000 rupees of outstanding

loans of farmer households. Among the states the proportion was highest in Bihar

(229 rupees per 1000 rupees), followed by Rajasthan (176 rupees per 1000 rupees).

• The most important source of loan in terms of percentage of outstanding loan amount

was banks (36%), followed by moneylenders (26%).

• Average outstanding loan per farmer household was highest in the state of Punjab,

followed by Kerala, Haryana, Andhra Pradesh and Tamil Nadu.

At the all India level around 49% of the farmer hhs. were indebted (col.2 in Table 3.1).

One can say that 51% of the farmer hhs. are financially excluded. These exclusion levels vary

from state to state. For example, it can be concluded that Andhra Pradesh has the highest

percentage of financial inclusion (82% of are indebted in A.P.). On the other hand, Meghalaya

has the lowest percentage of financial inclusion (only 4% of are indebted). These are misleading

because the indebtedness here includes both formal and informal sources. One can not include

loans from money lenders and traders under financial inclusion.

The percentage of indebted farmer hhs. by source of loan (cols.3 and 4 in Table 3.1)

shows 56% of indebted farmer hhs. obtain loan from formal sources and 64% from informal

sources. The total percentage is more than 100 (120%) because farmers take loans from multiple

sources. Approximately, we can say that only 56% of the indebted farmer hhs. are financially

included as they are getting loans from formal sources. The shares in formal and informal

sources vary from state to state. In Andhra Pradesh, 54% of the indebted farmer hhs obtain loans

from formal and 77% from informal sources (total is 130%).

Table 3.1 also gives another distribution by formal and informal sources (Cols.5 and 6). This

gives distribution of outstanding loan by sources. Table indicates that if a farmer’s outstanding

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loan is Rs.100, around Rs.57.7 is from formal sources and Rs.42.4 is from informal sources.

These percentages provide interesting information at state level. For example, the percentage of

loan from formal sources in Chattisgarh, Jharkhand, Orissa and Uttar Pradesh is more than 60%

and higher than that of all India. On the other hand, only 31% of loan is obtained from formal

sources in Andhra Pradesh. Therefore, source of loan is important for examining the extent of

financial inclusion.

Table 3.1. Percentage of indebted farming hhs all sources of loan, by source of loan and distribution of outstanding loan by source of loan: 2003

State

Percentage of indebted farming

hhs in the total rural hhs. (all sources)

Percentage of Indebted farmer hhs. by source of loan*

Percentage distribution of out standing loan by

sources

Formal Informal Formal Informal

1 2 3 4 5 6Andhra Pradesh 82 54 77 31.4 68.5Arunachal Pradesh 6 14 103 26.9 73.1Assam 18 15 88 37.5 62.6Bihar 33 23 84 41.7 58.5Chhattisgarh 40 66 56 72.4 27.7Gujarat 52 63 49 69.5 30.5Haryana 53 76 50 67.6 32.5Himachal Pradesh 33 57 65 65.3 34.7Jammu&Kashmir 32 9 94 67.6 32.3Jharkhand 21 44 60 64.1 35.9Karnataka 62 57 55 68.9 31.2Kerala 64 96 40 82.3 17.6Madhya Pradesh 51 64 66 56.9 43.0Maharashtra 55 92 30 83.8 16.2Manipur 25 6 99 18.2 81.9Meghalaya 4 2 97 6.0 94.0Mizoram 24 33 67 77.3 22.6Nagaland 37 20 79 68.8 31.1Orissa 48 68 46 74.8 25.1Punjab 65 58 70 47.9 52.1Rajasthan 52 38 81 34.2 65.8Sikkim 39 18 89 57.8 42.2Tamil Nadu 75 59 67 53.4 46.5Tripura 49 46 55 79.7 20.3Uttar Pradesh 40 47 70 60.3 39.7Uttaranchal 7 65 44 76.1 23.9West Bengal 50 51 73 58.0 42.1Group of UTs 51 42 71 59.0 41.0All India 49 56 64 57.7 42.4Note:Formal and Informal is more than 100% because farmers borrow from multiple sources.Source: Calculated from NSSO (2005)

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Another issue is the inclusion of credit for small and marginal farmers. Table 3.2 shows that the

share of formal source increases with the size of land. At all India level, the share of formal

source varies from 22.6% to 58% for small and marginal farmers while it varies from 65 to 68%

for medium to large farmers. Dependence of small and marginal farmers on informal sources is

high even in states like Andhra Pradesh, Punjab and Tamil Nadu. For example, small and

marginal farmers of Andhra Pradesh have to depend on 73% to 83% of their loans on informal

sources. This indicates very low financial inclusion for Andhra Pradesh. The NSS data also

shows that across social groups, the indebtedness through formal sources is lower for STs as

compared to others.

Table 3.2: Percentage Distribution of outstanding loans by formal and informal source across size classes of land in selected states: 2003

State

Size Class of Land owned

<0.010.0 I - 0.40

0.40 - 1.00

1.01 - 2.00

2.0 I - 4.00

4.01 -10.00 10.00+

All sizes

Foraml SourcesAP 16.9 19.3 25.1 26.6 41.5 48.6 49.5 31.4Bihar 36.5 20.8 47.0 66.1 63.4 19.6 70.1 39.2Maharashtra 58.3 83.2 80.2 78.8 83.8 88.7 91.1 83.8Orissa 64.7 62.4 77.1 72.1 88.4 96.9 13.2 74.8Punjab 24.8 29.2 65.6 49.1 61.2 47.5 30.1 47.9Tamil Nadu 19.1 37.4 46.0 61.5 65.2 74.3 82.9 53.4All India 22.6 43.3 52.8 57.6 65.1 68.8 67.6 57.7 Informal SourcesAP 83.2 80.9 75.0 73.4 58.4 51.4 50.5 68.5Bihar 63.5 79.2 53.0 33.8 36.6 80.4 29.9 58.5Maharashtra 41.6 16.8 19.8 21.1 16.2 11.3 8.9 16.2Orissa 35.4 37.5 22.8 27.9 11.7 3.2 86.8 25.1Punjab 75.2 71.0 34.5 50.9 38.8 52.4 70.0 52.1Tamil Nadu 80.9 62.5 53.9 38.6 34.7 25.7 17.2 46.5All India 77.4 56.7 47.2 42.4 34.0 31.2 32.8 42.3

Source: Calculated from NSSO (2005)

Similarly, there are many financially excluded households such as unorganized workers,

self employed, artisans and other vulnerable groups in both rural and urban areas3. Finance for

housing is another area where many poor are excluded.

3. 3. Supply and Demand Side Issues

It is being increasingly recognized that addressing financial inclusion require a holistic

approach addressing both supply and demand side aspects. Although there has been significant

3 On household indebtedness see NSS report no. 501, All India Debt and Investment Survey published in 2005

19

expansion in banking in the last few decades, there are many supply side problems for

commercial banks, RRBs and Co-operative Banks. Some of the criticisms on the trends in rural

credit in the 1990s are: (a) narrowing of the branch network in rural areas; (b) fall in credit-

deposit ratios in rural areas; (c) disproportionate decline in agriculture credit to small and

marginal farmers; (d) worsening of regional inequalities in rural banking – steepest decline in

credit-deposit ratio in eastern and north eastern states; (e) crippling RRBs4. Political interference

including loan waivers and write-offs also resulted in unviability and sickness in some of the

formal rural credit institutions.

One issue is whether we need separate institutions for promoting financial inclusion. Existing

formal institutions may be sufficient for this purpose. It is true that commercial banks have their

own problems. Man power shortage, unfavorable attitude towards rural services, infrastructure

and technology problems in rural areas etc. Rural banking has to be friendly to small and

marginal farmers and other vulnerable groups. It requires a specific type of organizational ethos,

culture and attitude (Rangarajan, 2005). The cadre of officers in rural branches has to develop

this attitude and promote financial inclusion of low income groups treating it both a business

opportunity as well as social responsibility. There is a need to remove the supply side problems

of commercial banks, RRBs and co-operative banks. As the last year’s Budget admits, ‘the

cooperative banks, with few exceptions, are in shambles’. This institution has to be revived as

many farmers are dependent on the credit from these banks. Vaidyanathan committee’s

recommendations may be helpful to revive cooperative sector.

So far we have been discussing mainly the issues relating to credit. Savings, insurance and other

financial services are also important. NSS data shows that around 88% of rural households in

2002 reported one or the other form of financial assets under ‘deposits’ which include deposit

accounts with banks, govt. certificates, post office deposit account, private deposits, insurance

policy and, cash in hand. However, it may be noted that only 6.82 crore households out of total

19.9 crore hhs. (around 36% of hhs.) were availing banking services to have a deposit account in

2001. Therefore, there is lot of scope for business opportunities for banks to include deposit-

excluded households.

4 More on this see Shetty (2003) and articles in Ramachandran and Swaminathan (2004).

20

The poor face many individual and covariate risks such as droughts, floods, cyclones, fires, theft,

pest attack, sharp fall in price, health problems, accident, death of family member etc. They need

some kind of insurance to cope with these risks. The supply of insurance mechanisms has

increased in the last decade. With the opening up of insurance to the private sector, the pricing of

insurance services will see some changes. Too much under pricing of these schemes by the

Government may not be sustainable for both Government and private sectors.

On demand side, some of the constraining factors for financial inclusion in rural and urban areas

are: low productivity and risk and vulnerability of small and marginal farmers, low skill and poor

market linkages for rural non-farm and urban workers, vulnerability to risk for rural landless and

urban poor, inadequate awareness and low financial literacy. In order to improve demand, the

suitability of existing financial products for the farmers/poor must be assessed. For example,

rural poor do not have even safe place to keep their savings let alone thinking about demand for

credit. Suitable mechanisms have to be explored for addressing the risks of the farmers and other

poor such as weather, price, yields, technology etc. Moreover, financial instruments have to be in

such a way that they undertake economically viable activities. The financial institutions have to

educate the poor and vulnerable by giving wide publicity to their financial instruments e.g. no

frills bank account.

3.4. Role of Self Help Groups, Micro Finance Institutions and other Initiatives

Reserve Bank of India recognized the problem of financial exclusion in the annual policy

statement in 2005 and since then has initiated several policies aimed at promoting financial

inclusion of especially pensioners, self employed and those employed in the unorganized sector5.

Some of these include ‘no frills’ banking account, a similified general purpose credit card

(GCC), introduction of pilot project for 100 per cent financial inclusion etc.

NABARD also has also taken several initiatives that have significantly contributed to financial

inclusion. Self Help group (SHG)-bank linkage programme of NABARD is an innovative

programme. It started as a pilot program in 1992. We have 22 lakhs SHGs under this program

5 More on the initiatives of RBI on financial inclusion, see Thorat, Usha (2006).

21

comprising more than 3 crores poor households who are accessing credit through commercial

and cooperative banks. Every year 6 lakh SHGs are added. The program is no longer confined to

Southern states. The non-southern states have 46% of the groups. Thus the SHG movement is

now a national movement.

There have been several institutional innovations in financial services by including civil society.

Followed by the success of SHG-Bank linkage programme as also Bangladesh Gramin Bank

model, many of the NGOs have taken to financial intermediation adopting innovative delivery

approaches. Following the RBI guidelines in 2000, commercial banks including RRBs have been

providing funds to micro finance institutions (MFIs) for on-lending to poor clients. Though

initially only handful of NGOs were into financial intermediation using a variety of delivery

methods, their numbers have increased considerably today. A large majority of MFIs operate on

much smaller scales with clients ranging from 500 to 1500 per MFI. However, a few non-

banking financial company (NBFC) MFIs have an outreach of more than one lakh. MFIs have

been playing an important role in substituting money lenders and reduce the burden on formal

financial institutions6. The competition created in the form of developing several non-banking

financial institutions in rural areas and SHG movement also reduced the interest rates in the

informal credit market7.

With the objective of ensuring greater financial inclusion and increasing the outreach of the

banking sector, banks have been allowed to use the services of NGOs, self help groups, micro

finance institutions and other civil society organizations as intermediaries in providing financial

and banking services through the use of business facilitator and correspondent models.

Provisions for this kind of financial intermediation have opened new and diverse avenues to

address the issue of financial inclusion by banks. NABARD also has some other initiatives like

joint liability group approach, Rytu Mitra Groups in Andhra Pradesh.

One can also learn lessons from successful experiences in and outside India. Within India, we

have good and successful practices for credit like Kudumbasree program in Kerala, Velugu

(Indira Kranti Padhakam) SHG program in Andhra Pradesh. We have good practices in SEWA

6 On the approach of RBI on micro finance, see Reddy (2005) 7 See Mahajan (2004)

22

(health), BASIX (livelihoods) for insurance while Pondicherry pilot project offers lessons for

bank accounts. We can also learn from the successful practices in countries like Bangladesh,

Thailand, Indonesia, Mexico and Brazil.

There are some issues which have to be sorted out regarding SHG movement and MFIs. Some of

these are: Are the SHGs really self help groups or are they receiving lot of subsidies from the

government or donors? What will happen if subsidies are removed ? Are the interest rates of

24% to 36% charged by MFIs justified? What types of terms and conditions are needed for better

functioning of MFIs?

23

SECTION 4PRICE POLICY AND FARM PROFITABILITY IN AGRICULTURE

Profitability in farming is important for sustainable agriculture development. With higher

profitability, savings can be improved and which in turn would raise investments in agriculture.

Price policy plays an important role in profitability. Here we examine the role of price policy in

improving profitability.

Agricultural price policy plays an important role in achieving growth and equity in Indian

economy in general and agriculture sector in particular. The major underlying objective of the

Indian government’s price policy is to protect both producers and consumers. Achieving food

security at both national level and household level is one of challenges in India today. Currently,

food security system and price policy basically consists of three instruments: procurement

prices/minimum support prices, buffer stocks and public distribution system (PDS). Agricultural

price policy is one of the important instruments in achieving food security by improving

production, employment and incomes of the farmers. There is a need to provide remunerative

prices for farmers in order to maintain food security and increase incomes of farmers. There has

been a debate on price vs. non-price factors in the literature. However, a review of literature

shows that they are complements rather than substitutes (Dev and Ranade 1998; Rao

2004&2006; Schiff and Montenegro 1997).

In the post-reform period, it was viewed that reforms in non-agriculture would shift terms of

trade (TOT) in favour of agriculture and lead to enhancement of private sector investment which

in turn would raise growth in agriculture (Singh 1995). The favourable TOT in agriculture have

some impact on agriculture in the post-reform period as the periods of improving TOT like the

early nineties and recent years after 2004, witnessed robust growth in agricultural production in

general and foodgrains in particular. However, the slackening of the efforts in case of non-price

factors affected growth of production in the recent period.

Food inflation of around 18-19 per cent in recent months is a concern particularly for the poor

and vulnerable. Several factors such as shortages in domestic supplies due to drought situation;

rise in international prices; shortages in global supplies mainly due to diversion of significant

foodgrains to biofuels; increase in demand due to higher growth, national rural employment

24

guarantee act (NREGA) and loan waiver scheme; inefficiencies in marketing system; speculation

etc. have been responsible for the price rise in cereals, pulses, sugar, fruits and vegetables, milk

etc. Increase in domestic supplies of agricultural production is important to provide food to the

poor and others at reasonable prices. Increase in supplies is also necessary for the success of PDS

system which is supposed to be an important instrument for food security at household level.

Prices and supply side non-price factors can enhance yields and provide higher incomes for the

farmers apart from providing food security for the poor.

The agricultural price policy has come under serious attack in recent periods on the grounds of

higher support prices than the costs of production warrant and supposed distortion of the market

leading to food deprivation. It is also blamed frequently for the spikes in prices of food items that

reached their peaks in 2009. Rice and wheat are the most state-protected crops and livelihoods of

many farmers are dependent on incomes from these crops, grown in an area of nearly 75 million

hectares or more than 40% of the gross sown area. Analysis of costs and returns in these crops

gives some idea about the profitability of Indian agriculture and provide insights into the

working of the price policy.

Against this background, the overall objective of this section is to examine the effectiveness of

price policy in helping farmers get sufficient profits to promote investment, technology and

productivity, thereby to the food security of the country. The specific objectives are to find

out the trends in the movements of costs, prices and returns in rice8 and wheat farming to throw

light on the impact of price policy on the profitability of farming in case of two of the most

cultivated and consumed food crops in the country. Also, it tries to bring out the causes that

necessitated recent increases in support prices and their relation to food security of the country.

The data generated under Cost of Cultivation Scheme (CS) of Directorate of Economics and

Statistics (DES), Ministry of Agriculture is used for the analysis in thissection. The data

collected annually under this scheme include all the major crops. This mine of data is largely

unexplored for policy relevant research and encompasses 9000 farmers every year. This data

help in analyzing the economics of cultivation of different crops as well as to see the

8 Rice and paddy are used interchangeably in the paper. Whenever we use rice it refers to paddy.

25

effectiveness of macro policies like price policy9. The costs and returns are calculated for all-

India using this data to see the emerging trends in profitability. The weights based on area and

productions of respective crops are developed to combine the data from states. We have used

area based weights for all the variables except cost of production. The growth rates used are

based on semi-log trend and deflation is done using consume price indices for agricultural

labourers of individual states. The study analyses data for rice and wheat for a period of more

than 25 years from 1981 to 2007-08 for all major growing states. However, 2006-07 is the last

year for which data for different states are available for rice and 2007-08 for wheat. We have

divided the study period into two periods roughly synchronizing with the pre liberalization and

liberalization eras. The first period starts with 1981 and ends with 1992-93 and the second period

cover the years from 1994-95 to 2007-0810. The costs and other data under CS data are

comparable over time except for a minor change in valuation of family labour. Since 1991,

family labour is valued at casual labour wages and not those of attached labour. Nevertheless, it

does not alter the overall conclusions of the analysis.

This section first presents the costs in cultivation and production of rice and wheat. Secondly, it

gives the movements of minimum support prices and prices realized. Third, it examines the

relationship among costs of production, support prices, prices realized and wholesale prices.

Fourth, the section examines the trends in returns at all-India and across different states. Lastly,

all these threads would be brought together to identify the causes for higher support prices in

recent years.

4.1. Trends in Costs and Yields

The trends in C2 cost of cultivation per hectare and C2 cost of production per quintal and A2

cost of cultivation for the period 1981-82 to 2007-08 for rice and wheat crops are examined

here11. There have been debates that rice should be given similar minimum support prices (MSP)

9 Sen and Bhatia (2004) and Raghavan (2008)10 1993-94 is excluded from analysis as too few surveys are done for that year.11 Cost A2 are paid out costs. They include: Value of hired labour (human, animal, machinery).Value of seed (both farm produced and purchased), Value of insecticides and pesticides,Value of manure (owned and purchased), Value of fertilizer, Depreciation on implements and farm buildings, Irrigation charges Land revenue, cesses and other taxes, Interest on working capital, miscellaneous expenses (Artisans etc.), rent for leased-in land.

Cost C2 ( overall cost of production) = A2 + Imputed costs, Imputed value of family labour (statutory wage rate or the actual market rate, whichever is higher), Rental value of owned land( net of land revenue) estimated on the basis

26

as compared to wheat as the costs of both the crops are similar. We examine this issue here by

looking at the trends in ratio of rice costs to wheat costs. The total costs of production per unit of

rice and wheat, which includes imputed values of land, labour and capital, shown in Table 4.1,

reveals that the unit costs of the former are somewhat lower than those of the latter. However,

the situation seems to have changed after 1994-95 and there are several years in which paddy

cost of production per unit exceeded that of wheat. This was particularly noticeable after 1999-

2000.

Table 4.1: Different Costs in the Production of Rice and Wheat at All-India LevelYears Rice Wheat Ratio of paddy cost

to wheat costCoP CoC A2

CoCCoP CoC A2

CoCCoP Co

CA2 CoC

1981-82 99 2892 1705 122 3260 1946 81 89 881982-83 116 2824 1680 125 3475 2065 93 81 81

1983-84 108 3351 1959 135 3462 2039 80 97 961984-85 113 3582 2107 133 3752 2121 85 95 991985-86 118 3718 1966 123 3959 2335 96 94 841986-87 124 3717 2240 132 4058 2391 94 92 941987-88 144 4653 2828 146 4826 2777 99 96 102

1988-89 1475704 3636

1685636 3292

87101 110

1989-90 1726340 3539

1725769 3361

100110 105

1990-91 185 6526 3734 197 6872 3800 94 95 98

1991-92 2187884 4161

2047693 4303

106102 97

1992-93 238 7684 3957 238 8808 4823 100 87 82

1994-95 27911212 6369

29410990 5446

95102 117

1995-96 306 11207 6324 318 11681 6100 96 96 1041996-97 338 12651 6703 361 13760 6927 94 92 97

1997-98 37013581 7246

38113236 6853

97103 106

1998-99 39815495 8710

38314316 7268

104108 120

1999-00 44216978 9275

41516459 8038

106103 115

2000-01 44817365 9798

45017132 8751

99101 112

2001-02 46918655 10619

46617279 9058

101108 117

2002-03 53019193 10949

49918837 10027

106102 109

2003-04 48319583 10988

49818925 10195

97103 108

2004-05 52920670 11776

53719810 10975

98104 107

of prevailing rents in the village for identical type of land or as reported by the sample farmers subject to the ceiling of ‘fair rents’ given in the land legislation of the concerned state. It varies between 30 and 33 percent of gross value of output (GVO ) Interest on value of owned fixed capital assets (excluding land) charged at 10% per annum.

27

2005-06 529 21182 11845 592 21847 11584 89 97 1022006-07 546 22059 12543 586 23847 12681 93 93 992007-08 NA NA NA 617 25575 13166 - - -

Source: Estimated by the authors based on CACP data at current pricesNote: CoP- Cost of production; CoC- Cost of cultivation;

The ratio of paddy cost of production to that of wheat is lower than the ratio of their cost

of cultivation because of higher yields in paddy. The ratio of A2 CoC of rice to wheat was higher

than the corresponding ratio of C2 CoC as shown in Table 4.1. This may be because of lower

imputed values of land, labour and capital in case of paddy compared to wheat. The conclusion is

that the costs of rice have been similar to those of wheat since the mid-1990s. The ratio came

down to 0.90 and 0.91 in the case of CoP in the years 2005-06 and 2006-07. On the whole the

demand that the MSP of rice should be closer or slightly below wheat based on cost data may

need sympathetic hearing. However, it may be noted that although cost is a major one, it is only

one factor among many factors in determining MSP.

The growth rates in the real costs of production declined in the background of a robust gain in

per hectare yields in the first period, while these costs went up in real terms in the second period

(Table 4.2). As can be seen from the table, the growth rate in yields came down from 2.67 to

0.86 in rice and from 2.54 to 0.52 in wheat in the first and second periods respectively. The

growth in yield outstripped growth in cost of cultivation during the eighties enabling the cost per

quintal to go down. The reverse can be noticed for the later period. Another important point to be

noted is that the cost of cultivation has grown at a lower rate in the recent period indicating that

the lower profitability might have discouraged farmers to invest in higher use of inputs and

technology.

Table 4.2: Trend Growth Rates of Different Costs and Yield in Rice and Wheat at All-India

Period

Rice Wheat

M.PPunjab

All-India

Haryana M.P

Punjab All-India

Cost of production (Constant prices)

1981-82 to 1992-93 2.95 -1.52 -0.13 -6.171.7

7 -2.58 -1.96

1994-95 to 2006-07 3.31 -0.50 1.46 2.080.9

7 0.65 1.41Cost of cultivation (Constant prices)

1981-82 to 1992-93 4.14 -1.55 2.32 -0.563.7

4 0.55 1.36

1994-95 to 2006-07 -0.51 2.18 1.92 2.212.9

4 1.35 1.96A2 Cost of cultivation (Constant prices)

28

1981-82 to 1992-93 4.62 -3.31 3.40 -1.294.3

1 -0.22 0.72

1994-95 to 2006-07 -0.33 2.23 2.15 3.012.7

4 1.22 2.45Yield (Qtls/ha)

1981-82 to 1992-93 1.13 -0.10 2.67 3.732.4

6 2.16 2.54

1994-95 to 2006-07 -3.63 2.76 0.86 0.212.0

2 0.87 0.52Note: The second period extends up to 2007-08 for wheat

Source: As in Table 1

Table 4.3: Cost of Production of Different States in Relation to All-India Average for Rice and Wheat

State Rice WheatTE

1984-85

TE 1996-

97

TE 2006-

07

TE 1984-

85

TE 1996-

97

TE 2007-

08A.P 93 92 73 - - -Assam 88 114 126 - - -Bihar 110 109 96 - 114 102Chattisgarh - - 94 - - 149Gujarat - - - - 133 100H.P 102 - 50 121 130 109Haryana 111 124 106 103 78 84Jharkhand - - - - - 187Kerala - - 119 - - -Karnataka 92 - 105 - - -M.P 102 109 138 95 122 116Orissa 84 96 104 - - -Punjab 105 96 77 98 92 84Rajasthan - - - 104 85 77T.N - - 128 - - -U.P 102 80 96 98 86 87Uttarakhand - - - - - 103W. Bengal 119 117 121 - - 157All-India 100 100 100 100 100 100

Source: Calculated from CACP Reports

Which states are relatively efficient in costs of production relative to all-India average? The

states of HP, AP and Punjab are the efficient producers of rice in the triennium ending 2007

(Table 4.3). The farmers of AP and Punjab could produce a quintal of rice at 27% and 23%

lower cost than that of the all-India average and they have improved efficiency of production by

reducing the cost of production relative to all-India average during the study period.. The obverse

29

is true in case of Assam and M.P. Madhya Pradesh produces rice at 30 per cent higher costs.

Also, farmers from Assam and Tamil Nadu are expensive in rice production, which may be

impinging on their profitability seriously. Rajasthan, Punjab and Haryana are the efficient

producers compared to all-India average for wheat. Here, Jharkhand, W. Bengal and Chattisgarh

produce wheat at whopping 87%, 57% and 49% higher cost than all-India.

4.2. Trends in MSPs and Prices Realised by Farmers Here we examine the trends in minimum support prices (MSPs) and the prices realized by

farmers. The CACP reports provide implicit prices which are derived from the CS data for

different states. Implicit price is the ratio of value of the output of main product per hectare to the

yield per hectare. It is known the cost and production given by the CS are the reported data by

the farmers. In other words, the implicit prices reflect the prices realized by the farmers.

Changes in MSP

The changes in MSP show that the increase in rice and wheat prices were the highest during the

period 2000-01 to 2009-10 as compared to those of earlier decades (Tables 4.4). Rice prices for

common variety increased from Rs.510 to Rs.1000 while wheat prices rose from Rs.580 to

Rs.1080 during this period. In the 1990s, the rate of increase in MSP of rice was lower than that

of wheat. The annual changes reveal that MSP increased significantly in the first few years after

the reforms were introduced. Again it rose substantially during 2006-07 to 2009-10. Rice and

wheat prices have risen respectively by 62 per cent and 54 per cent during this period.

Table 4.4: Trend Growth Rates in MSPs for Rice and Wheat in Real TermsPeriod Rice Wheat

1981-82 to1990-91 -0.95 -2.221990-91 to 2000-01 0.99 2.232000-01 to 2009-10 1.81 1.30

Source: Calculated from CACP reports

Table 4.5: Inter Crop Price Parity of MSPYear Common

paddy/wheatGrade Apaddy/ wheat

Year Common paddy/wheat

Grade Apaddy/ wheat

1981-82 0.89 na 1996-97 1.00 1.041982-83 0.86 na 1997-98 0.87 0.94

1983-84 0.87 na 1998-99 0.86 0.921984-85 0.90 na 1999-00 0.89 0.951985-86 0.90 na 2000-01 0.88 0.93

30

1986-87 0.90 0.93 2001-02 0.87 0.921987-88 0.90 0.93 2002-03 0.89 0.941988-89 0.93 0.98 2003-04 0.89 0.941989-90 1.01 1.07 2004-05 0.89 0.941990-91 0.95 1.00 2005-06 0.89 0.941991-92 1.04 1.09 2006-07 0.89 0.931992-93 0.98 1.02 2007-08 0.88 0.911993-94 0.94 1.00 2008-09 0.90 0.931994-95 0.97 1.03 2009-10 0.93 0.951995-96 1.00 1.04

Note: MSP includes bonus Source: CACP Reports

The inter-crop price parity between rice and wheat shows that the ratio of paddy to wheat

increased from 0.89 in 1981-82 to around 1.0 in 1989-90 (Table 4.5). It ranged between 0.94 and

1.04 during 1989-90 to 1996-97. The ratio declined significantly in 1997-98 because of sharp

rise in MSP for wheat. The MSP of wheat increased by 25% compared to 12.7% rise for rice in

that year. This increase in the form of bonus for wheat distorted the inter-crop price parity. It was

below 0.90 from 1997-98 to 2007-08. Only in the last two years the ratio reached 0.90 and

beyond. Similar trends can be seen for the ratio of grade A paddy to wheat.

Trends in Prices Realized by Farmers Farmers are interested in prices realized by them than MSP per se. The ratio of price realized to

MSP was higher than 1.0 for rice and wheat almost during the entire period (Table 4.6). Only in

the case of rice, it was lower than 1.0 during 2000-01 to 2003-04. In the subsequent years the

ratio was closer to one. On the other hand, the prices realized by farmers were more than MSP

for wheat in all the years (except 2001-02) during the period 1981-82 to 2007-08.

Growth rates of prices realized in real terms show that rice prices showed a declining trend in

both periods (Table 4.7), while wheat prices showed a positive growth rate and increased in the

second period. In other words, prices realized by wheat farmers have been higher and increasing

as compared to that of rice farmers.

Table 4.6: Price Realised in Relation to Minimum Support Prices in Rice and WheatYears Price realized Ratio of price

realized to MSPRice Wheat Rice Wheat

1981-82 121 151 1.05 1.161982-83 151 165 1.24 1.16

1983-84 151 160 1.14 1.061984-85 145 165 1.06 1.091985-86 163 173 1.15 1.101986-87 162 175 1.11 1.081987-88 191 202 1.27 1.221988-89 199 214 1.24 1.24

31

1989-90 211 221 1.14 1.211990-91 221 257 1.08 1.201991-92 283 332 1.20 1.481992-93 289 345 1.07 1.251994-95 363 388 1.07 1.111995-96 385 413 1.07 1.151996-97 416 531 1.09 1.401997-98 429 517 1.03 1.091998-99 494 563 1.12 1.101999-00 516 612 1.05 1.112000-01 477 586 0.94 1.012001-02 484 589 0.91 0.972002-03 511 625 0.93 1.012003-04 516 626 0.94 1.012004-05 557 648 0.99 1.032005-06 561 761 0.98 1.192006-07 609 898 0.98 1.282007-08 n.a 1018 n.a 1.20

Note: n.a: not available

Table 4.7 :Trend Growth Rates of Price Realised in Rice and Wheat

Period

Rice Wheat

M.P Punjab

All-India

Haryana M.P

Punjab

All-India

1981-82 to 1992-93 1.04 -0.92

-0.64 -1.01 -0.07 -1.41 -0.51

1994-95 to 2006-07 0.88 0.50

-0.35 1.74 2.36 1.71 1.71

Note: The growth rates for wheat in case of the second and third periods go up to 2007-08

Regional Disparities in Price RealisationThere are significant regional disparities when we consider the ratio of price realized to MSP.

There was a decline in the ratio in the triennium ending 2006-07 at the all-India level and in

several states excluding Punjab, HP and Haryana for rice. It was much lower in states like

Orissa, Bihar, Assam, WB and UP (Table 4.8). In the case of Haryana, the ratio was higher by

32% in the same triennium, which means that the realized price is 32% higher than respective

support price. The ratio for wheat was much higher than for rice. For example, the realized price

for wheat was 22% higher as compared to MSP at all India level in the TE 2007-08. The higher

ratio for wheat is true for all the reported states.

Table 4.8: Price Realised Relative to Minimum Support Prices in Rice and Wheat in Different States (in percentage)

State Rice WheatTE

1984-85

TE 1996-

97

TE 2006-

07

TE 1984-

85

TE 1996-

97

TE 2007-

08A.P 107 110 104 - - -Assam 103 105 94 - - -Bihar 147 109 86 - 137 121

32

Chattisgarh - - 102 - - 138Gujarat - - - - 156 124H.P 110 - 127 124 127 121Haryana 109 122 132 105 111 116Jharkhand - - - - - -Kerala - - 122 - - 123Karnataka 124 - 110 - - -M.P 110 110 114 123 135 140Orissa 116 101 85 - - -Punjab 106 106 107 105 111 116Rajasthan - - - 119 132 127T.N - - 101 - - -U.P 103 105 98 106 121 118Uttarakhand - - - - - 112W. Bengal 127 112 95 - - 109All-India 115 108 99 110 122 122

Source: Calculated from CACP Reports

4.3. Relationship between Costs, Prices Realised and MSP

In this sub-section, we compare the trends in costs, realized prices, MSP and wholesale prices.

The trends in cost of production and price realized for rice show that the latter moved faster than

the former till around 2000-01 (Figure 4.1). Later the prices realized were almost similar to cost

of production without any margin except in 2006-07. On the other hand, the prices realized by

farmers for wheat have always been higher than cost of production (Figure 4.2). Particularly, the

margins have been higher since the mid-1990s and more so in the last three years of the study i.e.

2005-06 to 2007-08.

Figure 4.1: Cost of Production and Price Realised in Rice during 1981- 82 to 2006-07

33

0

100

200

300

400

500

600

700

1981

-82

1982

-83

1983

-84

1984

-85

1985

-86

1986

-87

1987

-88

1988

-89

1989

-90

1 99

0-91

1991

-92

1992

-93

1994

-95

1995

-96

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

CoP Price realised

Another issue is growth in cost of production relative to the respective wholesale price

indices (WPI). The wholesale price index for rice increased from 100.0 in 1981-82 to

478.5 in 2006-07. The index of cost of production shows that it was moving almost on

par with WPI till 2001-02. In the last five years of the study i.e. 2002-3 to 2006-07, the

CoP has risen faster than WPI (Figure 3). Here rice farmers were in difficult situation in

terms of CoP compared to WPI. The index of MSP of rice increased from 100 in 1981-82

to 539.1 in 2006-07. The growth in MSP is almost similar to that in cost of production till

2001-02 after which spikes in cost of production are much higher relative to the MSP

(Figure 4.3). As shown later, the increase in MSP in 2007-08 and 2008-09 was much

higher than costs and WPI.

Figure 4.2: Cost of Production and Price Realised in Wheat during 1981-2007-08

34

0

200

400

600

800

1000

1200

1981

-82

1982

-83

1983

-84

1984

-85

1985

-86

1986

-87

1987

-88

1988

-89

1989

-90

1 99

0-9

1

1991

-92

1992

-93

1994

-95

1995

-96

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

CoP Price realised

Figure 4.3: Indices of Cost of Production, MSP and Wholesale Prices in Rice

0

100

200

300

400

500

600

1981

-82

1982

-83

1983

-84

1984

-85

1985

-86

1986

-87

1987

-88

1988

-89

1989

-90

1 99

0-91

1991

-92

1992

-93

1994

-95

1995

-96

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

CoP MSP WPI

The index of cost of production rose from 100 to 505.8 only during the same period for

wheat (Figure 4.4). The WPI, MSP and CoP changes were similar till the early 1990s.

Thereafter, the MSP and WPI were always higher than CoP for wheat, especially after 1997-98.

In other words, input costs including imputed costs were lower than output prices for wheat crop

and the margins were higher for wheat as compared to rice.

Figure 4.4: Indices of Costs of Production, MSP and Wholesale Prices in Wheat

35

0

100

200

300

400

500

600

700

1981

-82

1982

-83

1983

-84

1984

-85

1985

-86

1986

-87

1987

-88

1988

-89

1989

-90

1 99

0-91

1991

-92

1992

-93

1994

-95

1995

-96

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

CoP MSP WPI

4.4 Returns to Farming

Ultimately one has to look at trends in profitability in order to examine the viability of farming.

For this purpose, we have examined the trends in net income (gross value of output-cost C2) and

farm business income (gross value of output-Cost A2). We also looked at trends in the ratio of

gross value of output to C2 cost, the ratio of gross value of output to A2 cost, which gives the

level of margin over total costs and variable costs, respectively..

Table 4.9: All-India Costs and Returns in Rice and Wheat per Hectare in Nominal Terms (in Rs.)

Year Rice WheatNI FBI GVO/CoC GVO/A2

CoCNI FBI GVO/CoC GVO/A2

CoC1981-

82561 1748

1.19 2.03558 1872

1.17 1.961982-83 626 1770 1.22 2.05 828 2238 1.24 2.081983-

841058 2451

1.32 2.25486 1909

1.14 1.941984-

85898 2373

1.25 2.13714 2173

1.19 2.111985-

861326 3078

1.36 2.571152 2775

1.29 2.191986-

871049 2526

1.28 2.131044 2711

1.26 2.131987-

881262 3088

1.27 2.091181 3230

1.24 2.161988-

891838 3906

1.32 2.07942 3286

1.17 2.001989- 1143 3944 1.18 2.11 1131 3540 1.20 2.05

36

901990-

911137 3929

1.17 2.051400 4472

1.20 2.181991-

922026 5748

1.26 2.383053 6443

1.40 2.501992-

931643 5370

1.21 2.362869 6854

1.33 2.421994-

953170 8014

1.28 2.262757 8301

1.25 2.521995-

962686 7569

1.24 2.202622 8203

1.22 2.341996-

972603 8551

1.21 2.284984 11818

1.36 2.711997-

981985 8320

1.15 2.153876 10260

1.29 2.501998-

993513 10298

1.23 2.185403 12450

1.38 2.711999-

002737 10440

1.16 2.136161 14582

1.37 2.812000-

011389 8957

1.08 1.914312 12692

1.25 2.452001-

021023 9060

1.05 1.853905 12127

1.23 2.342002-

03-9.0 8236

1.00 1.753606 12598

1.19 2.242003-

041661 10256

1.08 1.933919 12801

1.21 2.242004-

051382 10277

1.07 1.873215 12228

1.16 2.102005-

061561 10897

1.07 1.924656 15086

1.21 2.292006-

072867 12472

1.13 1.999655 20982

1.40 2.642007-

08n.a n.a

n.a n.a1324

425590

1.52 2.94Note: NI- Net income; FBI- Farm business income; FL- Family Labour; GVO- Gross value of output; CoC-

Cost of cultivation

The ratios of gross of value of output (GVO) to costs show that the value of output has

been more than all the costs throughout the period for both rice and wheat (Table 4.9). The

averages given in Table 4.10 show that the ratio of GVO to C2 cost for rice has been maintained

around 1.25 till 1995 but declined to 1.17 in 1996-2000 and to 1.07 in 2001-07. If we take the

ratio of GVO to A2 cost for rice, gross value of output has been twice to variable costs viz., A2

cost in most of the years except in the last seven years (Table 4.10).

Table 4.10: Ratios of Gross Value of Output to Costs (averages) Period Rice Wheat

GVO/C2CoC

GVO/A2CoC

GVO/C2CoC

GVO/A2CoC

1981-82 to 1985-86 1.27 2.21 1.21 2.06

37

1986-87 to 1990-91 1.24 2.09 1.21 2.10

1991-92 to 1995-96 1.25 2.30 1.30 2.45

1996-97 to 2000-01 1.17 2.13 1.33 2.64

2001-02 to 2006-07 1.07 1.89 1.23 2.31

1981-82 to 1992-93 1.25 2.19 1.24 2.14

1994-95 to 2006-07 1.13 2.03 1.29 2.49

1981-82 to 2006-07* 1.19 2.11 1.26 2.33

* Note: The ratios of GVO with C2 and A2 cost of cultivation for wheat are 1. 27 and 2.40 respectively during 2001-2008

The profitability of rice seems to have been going down, while wheat farmers

improved their profitability during 1981 to 2007. If we consider C2 costs, the rice farmers could

get only nine percent returns over their total cost of production in the TE 2006-07 when wheat

farmers got 26 per cent net returns over costs (Table 4.9). Significantly, the wheat farmers reaped

more than 50% margin over total costs in 2007-08. Though their counterparts in rice cultivation

could get 13% margin in 2006-07 and probably slightly higher in the later years, it still is

nowhere near that for wheat farmers.

In contrast to rice, the ratio of GVO to C2 cost for wheat increased over time. The ratio

increased from 1.21 in 1981-85 to 1.33 to during 1996-2000. The ratio of GVO to A2cost has

also risen as compared to the early 1980s. This profitability ratio was around 2.6 in the triennium

ending in 2007-08 (Table 4.10). It may be noted that this ratio for wheat was 2.41 and much

higher than that of rice at around 1.9 in the TE in 2006-07.

Profitability across States

The returns over C2 costs show that the states like Assam, Bihar, Karnataka, MP, Orissa, TN,

UP, and WB witnessed negative returns for rice in the latest triennium (Table 4.11). On the other

hand, all states covered all costs for wheat except for Jharkhand and WB. The profitability

improved for rice in AP, HP, Haryana and Punjab during the study period, while it declined for

other states. On the other hand, returns for wheat rose for all the states considered in the study.

Table 4.11: Ratio of Returns to Total Costs in Rice and Wheat in Different States S

tateRice Wheat

TE 1984-

85

TE 1996-

97

TE 2006-

07

TE 1984-

85

TE 1996-

97

TE 2006-

07

38

A.P 1.48 1.59 1.72 - - -Assam 1.3 1.06 0.83 - - -Bihar 1.34 1.1 0.97 - 1.23 1.34Chattisgarh - - 1.15 - - 1.1Gujarat - - - - 1.41 1.58H.P 1.21 - 1.44 1.14 1.01 1.12Haryana 1.07 1.06 1.2 1.12 1.37 1.38Jharkhand - - - - - 0.8Kerala - - 1.01 - - -Karnataka 1.48 - 1.19 - - -M.P 1.21 1.13 0.92 1.32 1.17 1.37Orissa 1.27 1.18 0.93 - - -Punjab 1.15 1.19 1.33 1.17 1.21 1.39Rajasthan - - - 1.3 1.48 1.59T.N - - 0.91 - - -U.P 1.13 1.29 0.98 1.16 1.3 1.34Uttarakhand - - - - - 1.11W. Bengal 1.19 1.17 0.93 - - 0.95All-India 1.26 1.24 1.09 1.19 1.28 1.38

Note: The total costs are represented by C2 cost of cultivation Table 4.12: Ratio of Returns to Variable Costs in Rice and Wheat in Different States

State Rice WheatTE

1984-85

TE 1996-

97

TE 2006-

07

TE 1984-

85

TE 1996-

97

TE 2006-

07A.P 1.67 1.95 2.04 - - -Assam 3.06 2.64 1.93 - - -Bihar 3.16 2.24 1.67 - 2.31 2.21Chattisgarh - - 2.37 - - 1.85Gujarat - - - - 2.48 2.62H.P 2.43 - 3.84 3.62 2.8 2.67Haryana 1.75 2.23 2.26 1.85 3.04 2.96Jharkhand - - - - - 1.1Kerala - - 1.44 - - -Karnataka 2.8 - 1.9 - - -M.P 2.43 2.28 1.8 2.58 2.32 2.81Orissa 2.21 2.24 1.77 - - -Punjab 1.82 2.18 2.49 1.94 2.31 2.78Rajasthan - - - 2.41 3.06 3.23T.N - - 1.47 - - -U.P 2.19 2.84 1.89 1.95 2.47 2.39Uttarakhand - - - - - 0.91W. Bengal 2.23 2.39 1.81 - - 1.59All-India 2.14 2.25 1.93 2.04 2.52 2.62

Note: The variable costs are represented by A2 cost of cultivation

However, all the states cover variable costs (A2) in rice and wheat with the exceptions being

Uttarakhand for wheat (Table 4.12). The situation in Jharkhand is also not remunerative enough

to the farming community of wheat. The returns over variable costs for rice are much higher for

HP, Punjab, Haryana, Chattisgarh than other states. The returns for wheat are more than twice

over A2 costs for the major wheat producing states. The figures 4.5 and 4.6 shows that the ratio

of returns over total costs (C2) and variable costs were higher for wheat as compared to rice

since the mid-1990s.

39

Figure 4.5: Ratio of Returns to Total Costs in Rice and Wheat

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.619

81-8

2

1982

- 83

1983

- 84

1984

- 85

1985

- 86

1986

- 87

1987

- 88

1988

- 89

1989

- 90

1 99

0-9

1

1991

- 92

1992

- 93

1994

- 95

1995

- 96

1996

- 97

1997

- 98

1998

- 99

1999

- 00

2000

- 01

2001

- 02

2002

- 03

2003

- 04

2004

- 05

2005

- 06

2006

- 07

2007

- 08

Rice Wheat

Figure 6: Ratio of Returns to Variable Costs in Rice and Wheat

00.5

11.5

22.5

33.5

1981

-82

1982

-83

1983

-84

1984

-85

1985

-86

1986

-87

1987

-88

1988

-89

1989

-90

1 99

0-91

1991

-92

1992

-93

1994

-95

1995

-96

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

Rice Wheat

The higher profitability for wheat as compared to rice can also be seen in the growth rates

of returns in constant prices (Table 4.13). Rice recorded positive and high growth rates in net

income, farm business income and farm investment income in the first period (1981-82 to 1992-

93). However, it showed a negative growth rate in all these returns in the second period (1994-95

to 2006-07).

40

Table 4.13: Trend Growth Rates of Returns to Farming in Rice and Wheat in Real Terms

Period Rice WheatM.P Punjab All-

IndiaHaryana

M.P Punjab All-India

Net income1981-82 to 1992-93 n.c 2.06 1.00 17.01 -4.03 5.17 5.811994-95 to 2006-07 n.c 7.15 -31.53 0.79 10.07 6.15 2.37

Farm business income1981-82 to 1992-93 1.06 0.71 2.56 6.96 0.93 3.59 3.671994-95 to 2006-07 -5.35 3.87 -1.15 1.30 5.06 3.14 2.05

n.c: Not calculated as the state witnessed negative returns during this period. Note: The second period and the overall period go up to 2007-08 for wheat.

Source: As in Table 1

The growth rates of rice in farm business income were similar to those of wheat in the

first period. However, the major point of distress for paddy farmers is that the returns over paid

out costs also declined in the second period at 1.15% per annum. On the other hand, growth rates

in profitability for wheat recorded positive growth rates of more than 2% in both net income and

farm business income in the second period also. In spite of similar growth rates for yields, the

profitability for wheat is much higher than that of rice. This could be partly due to better

realization of prices for wheat. At the state level, the growth rates in returns for rice in Punjab

rose in the second period while M.P. showed negative returns in the same period. The growth in

rice for Punjab has risen in spite of decline in yields for the second period and this may be

because of the high level of yields even with some decline and higher price realization relative to

the support prices. In the case of wheat, the growth rates for M.P. increased while those of

Haryana declined in the second period. Although growth rates in returns declined for wheat in

Punjab, they were nearly 3% per annum in farm business income and above 2% for net income

in the second period.

Table 4.14: Projected Cost of Production and MSP: Rice and Wheat 1999-2000 to 2009-10Years Rice Wheat

Projected C2 Cost per qtl (in Rs.)

MSP (in Rs.)

MSP over cost (%)

Projected C2 Cost per qtl (in Rs.)

MSP (in Rs.)

MSP over cost (%)

1999-00 400.6 520 29.8 415.9 550 32.22000-01 429.3 540 25.8 448.7 580 29.32001-02 471.7 560 18.7 478.9 610 27.32002-03 505.2 560 10.9 483.3 620 28.22003-04 525.2 580 10.5 496.8 630 27.02004-05 530.9 590 11.1 515.6 640 24.12005-06 557.6 600 7.6 541.5 700 29.32006-07 569.5 650 14.1 573.6 850 48.2

41

2007-08 595.0 775 30.3 624.5 1000 60.12008-09 619.0 930 50.2 648.6 1080 66.52009-10 644.9 1030 59.7 741.0* 1100 48.4* Refers to modified cost C2 including transportation, insurance premium and marketing charges. Source: Various reports of CACP

The data on costs and returns of crops from Cost of Cultivation Scheme are available with a lag

and therefore actual cost data for the years 2008-09 and 2009-10 are not available to compare

with MSP data. Therefore, we have used projected cost data which is used by CACP for

recommending MSPs (Table 4.14). As can be seen from the table, the margin over cost declined

over time for rice from 30% in 1999-00 to 7.6% in 2005-06. But, the margin of MSP over cost

for rice rose significantly from 14% in 2006-07 to nearly 60% in 2009-10. As compared to rice,

the margins of MSP over C2 cost have been much higher for wheat except in 2009-10. The

margin for wheat over cost was around 67% in 2008-09. Therefore, it can be said that the recent

increases in support prices have the effect of ameliorating the distress of rice farmers.

4.5. Increased Role of Price Policy and Open Trade Necessitating Higher Support Prices Historically, agricultural price policy evolved to take care of the undue rises in prices to the vast

majority of vulnerable sections of population. After the formation of price commission, it has

always tried to maintain a balance between the interests of consumers and producers.

Nevertheless, the limits of the price policy in achieving these goals are recognized by the

government and other non-price interventions are used primarily for the purpose. While a large

network of public distribution system ensures cheap food to the needy with appropriate levels of

subsidy from time to time, a slew of policy initiatives are put in place to make farming profitable

enough to invest sufficiently in technology for improving productivity per unit of land so that

food security is not threatened. The policy aimed at encouraging higher production and the

resultant food produce should be available at lower prices. Both higher production and cheap

food are considered necessary for food security. Thus, the price policy remained subservient to

the overall societal goal of poverty reduction on the whole until the new economic policies are

introduced.

42

The higher emphasis and reliance on price policy in the nineties altered the situation

drastically12, as price interventions to the relative exclusion of non-price interventions marked the

new regime as pointed out by Sen (2001). As a consequence, the earlier policy of ‘low-input and

low-output’ prices shifted to ‘high-input and high-output’ prices (Acharya, 1997). On the other

hand, public investments on irrigation, research, exgtension and other related infrastructure went

down from 3.4% of agricultural GDP in early eighties to 1.9% in 2001-03. The private

investments, though increased initially, also stopped flowing in due to the operation of

complementarity between public and private investments, by late nineties. Technology

development, dissemination and adoption received a major setback due to this.

As a result of this policy shift, growth rates in yields have gone down and eventually costs of

production started rising. These rising costs necessitate higher support prices to sustain the long-

run margin of 20% over total costs. The analysis in this study brings out this phenomenon

clearly. The MSPs in real terms declined in the eighties and still returns to farming did continue

to be sufficient for the farming community. This is because costs of production of both rice and

wheat fell during that period as productivity improved at more than 2.5% per annum and

outstripped growth in cost of cultivation. On the other hand, the costs of production rose at the

rate of nearly 1.5% per annum in both the crops during nineties and beyond making growth of

higher MSPs needed to help the farmers maintain the same incomes. It is important to note here

that these higher support prices are meant to compensate the slow-down in yield growth and

consequent increase in cost of production that is the result of dwindling non-price interventions

through public investments. In this situation, if the MSPs are not hiked sufficiently as in case of

rice in late nineties and early years of the new millennium, margins go down and distress

spreads. The analysis in the paper shows that their farm business income in real terms declined

by 1.15% per annum for rice farmers. To sum-up, the farming community is not necessarily

better off as a result of higher support prices, as these prices are meant to compensate for the

rising cost of production in the absence of yield increasing public investments.

The second major factor in driving higher support prices is the operation of market forces in a

liberal and open trade regime. The price policy faces different challenges in such a scenario. For

example, low production can coincide with low prices with liberalized imports and exports. 12 Rao (2001) provides a detailed exposition of the changes in the agricultural price policy

43

When the international market prices are higher and rising as a result of any supply shock,

domestic prices of the respective commodity shoots up and procurement of sufficient quantities

to the required levels to ensure food security becomes difficult. Therefore, the government will

have to offer higher prices as has happened in 1997 and 2007 and 2008 in case of wheat, making

the gross margin to more than 50%13. The pulls and pressures of democracy and farmer lobbies

make it impossible to roll back these prices without very high political costs, even if global

prices recede considerably. The forced unidirectional movement of support prices also has an

advantage in that assured prices and continuity in price structure can only stimulate supply

response for agricultural commodities.

The result of these higher support prices is that it hurts the consumers and has adverse impact on

poverty reduction14. It was estimated by Parikh et al (2003) that a 10% increase in MSPs of

wheat and rice leads to a decline in overall GDP by 0.33%, increase in aggregate price index by

1.5 per cent, reduction in investments by 1.9% and miniscule impact on agricultural GDP. They

also conclude that the bottom 80% of the rural and all of urban population is worse off. The

experience of the past few years clearly reveals that the option of trade for food security has

limited scope in view of the huge demands of a large population of the country. This means that

the balance between price and non-price interventions has to be struck as in earlier decades.

Therefore, non-price interventions through public investments have to be accelerated to reduce

the cost of production and thereby need for higher support prices. Also, system of variable tariffs

has to be implemented to insulate from the impacts on domestic prices of higher volatility in

international food market.

13 It is also documented by some scholars. See for example, Chand (2010)14 Sen (1999) explains vividly the vicious circle of low public investments, low yield growth, higher support prices, lower poverty reduction in the nineties quite well.

44

SECTION 5TRENDS IN PRIVATE INVESTMENT IN AGRICULTURE USING

MACRO LEVEL DATA

In this section, we examine the trends in private investment in agriculture using macro level data.

First we look at investment using National Accounts data. Then we examine trends in private

investment in agriculture using All India Debt and Investment Surveys (AIDIS). It is well known

that both public and private investment are needed for agriculture as they are non-substitutable.

The share of private investment in total investment increased significantly over time from about

50% in the early 1980s to 80% in the decade of 2000s (Table 5.1). It may be noted that 90% of

the private investment is made by farmers for on-farm production. Therefore, there is a need

for increasing on –farm investment.

Table 5.1: Investment in Agriculture

Investment in Agriculture

(crores)

Share of private

investment

Public Private Total (%)

In 1999-00 constant prices

1980-81 13174 15384 28558 53.9

1981-82 12723 11549 24272 47.9

1982-83 12665 13467 26132 51.4

45

1983-84 12962 14816 27778 53.3

1984-85 12488 12938 25426 50.8

1985-86 11248 12960 24208 53.5

1986-87 10667 13052 23719 55.0

1987-88 10981 17816 28797 61.9

1988-89 10302 15564 25866 60.2

1989-90 8909 17132 26041 65.8

1990-91 8938 29116 38054 76.5

1991-92 7901 16634 24535 67.8

1992-93 8167 22863 31030 73.7

1993-94 8907 19230 28137 68.3

1994-95 9706 17184 26890 63.9

1995-96 9560 17776 27336 65.0

1996-97 9225 20589 29814 69.5

1997-98 7812 24692 32504 76.0

1998-99 7949 24956 32905 75.8

1999-00 8668 41483 50151 82.7

2000-01 8085 37395 45480 82.2

2001-02 9712 47266 56978 83.0

2002-03 8734 46934 55668 84.3

2003-04 10805 42737 53542 79.8

In 2004-05 prices

2004-05 16183 62665 78848 79.5

2005-06 19909 73211 93121 78.6

2006-07 22978 71422 94400 75.7

2007-08 23039 86967 110006 79.1

2008-09 24452 114145 138597 82.4

The growth rates of investment show that public sector investment showed a negative growth in

the 1980s and 1990s and a growth of 15 per cent in 2000s (Table 5.2 and Fig 5.1). On the other

hand, growth rate of private investment increased gradually from 2.5% in the 1980s to 4.1% in

the 1990s and 5.2% in 2000s. On the whole, the growth rate of public and private investment is

the highest in the decade of 2000s.

Table 5.2: Growth Rates of Investment in Agriculture

Public Private Total1981 to

1990 -3.804952.50217

7-

0.255781991 to

2000 -0.220194.11180

93.05240

42001 to

2009 15.73935.15175

27.39538

1

46

Figure 5.1: Growth Rates of Investment in Agiculture

One indicator for investment is the share of gross capital formation (GCF) in agriculture as a

proportion of agricultural GDP. This ratio has been stagnant at around 14 per cent during 2004-

05 to 2006-07 (Table 5.3). However, there has been a significant increase in the 11 th five year

plan period. The ratio increased to 16.3% in 2007-08 and further to 19.67% in 2008-09 and to

20.30% in 2009-10. However, the share of GCF in agriculture in overall GDP has remained

stagnant at around 2.5 to 3.0% (Table 5.3). Due to this, the share of GCF in agriculture and allied

sector in total GCF has remained in the range of 6.5 to 8.2% during 2004-05 to 2009-10 (Table

5.4)

Table 5.3. Gross Capital Formation (GCF) in Agriculture and Allied Activities (Rs.crore at 2004-05 prices)Year GDP Agriculture &allied activities GCF/GDP in

agricultureGCF in agriculture as per cent of total GDP

-- GCF GDP -- --2004-05 2971464 76096 565426 13.46 2.562005-06 3254216 86611 594487 14.57 2.662006-07 3566011 90710 619190 14.65 2.542007-08 3898958 105034 655080 16.03 2.692008-09P 4162509 128659 654118 19.67 3.092009-10 4493743 133377 656975 20.30 2.97Source: Economic Survey, 2010-11

Table 5.4 Share of Agriculture and Allied Sector’s GCF (per cent) at 2004-05 pricesYear GCF agriculture/total GCF (%)2004-05 7.52005-06 7.32006-07 6.62007-08 6.52008-09 8.32009-10 7.7

47

Source: Economic Survey, 2010-11

5.1. Trends in On-farm private investment : All India Debt and Investment Surveys

Here we examine the trends in on-farm investment based on All India Debt and Investment

Surveys. The first comprehensive survey on rural credit was the All-India Rural Credit Survey,

1951-52. RBI carried out the second decennial survey known as All-India Rural Debt and

Investment Survey (AIRDIS), 1961-62, with the objective of arriving at statistically valid

estimates of various economic characteristics of rural households. These two surveys conducted

by the RBI though mainly confined to rural households forms the basis of further decennial

surveys. However, because of various administrative and other attending problems encountered

in the above surveys, the field work of the next survey was entrusted to NSSO in 1971-72. Scope

of this survey was extended to cover urban households also. NSSO conducted the next four

surveys as part of their 26th round (1971-72), 37th round (1981-82) , 48th round (1991-92) and 59th

round (2003).

The last two rounds 1991-92 and 2003 surveys are more comparable than the earlier surveys. We

examine the trends using these two surveys. In 2003 survey, livestock is added. Table 5.6

provides percentage distribution of fixed capital expenditure of households in farm business in

1991 and 2003 excluding livestock. The trends in composition show that there was a significant

increase in the share of farm houses, wells and irrigation while the shares of land improvement

and transport equipment declined. There was also some improvement in the share of agricultural

machinery. It indicates that the growth of capital on farm houses, wells and other irrigation and

agriculture machinery is much higher than land improvements and transport equipment. If we

concentrate on composition in 2003, farmers invest the highest amount in wells and other

irrigation followed by agricultural machinery, transport equipment and land improvements. It is a

matter of debate whether farmers should invest more in well irrigation too much when the

ground water is depleting in many areas. They should invest in areas where ground water is

plenty.

There are significant variations in the compositional shifts across states (Table 5.6.). The

findings on state level are the following.

(a) The share of land improvements declined in all but four states.

48

(b) In the case of orchards and plantations, Kerala is the only state which has the largest

share although the share declined between 1991 and 2003. There was a significant

increase in the shares of three Southern states viz., Andhra Pradesh, Karnataka and Tamil

Nadu and also Assam.

(c) The share of farm buildings increased in all states except in Haryana. Particularly, the rise

was very high in Assam, Bihar, Maharashtra, Rajasthan, U.P. and West Bengal.

(d) In eight states, the share of wells and irrigation increased significantly.

(e) There was a decline in the share of agriculture machinery for nine states.

(f) Similarly, the share of transport equipment declined in 11 states.

(g) Regarding the composition of capital in 2003, seven states viz., Andhra Pradesh,

Haryana, Karnataka, Maharashtra, Punjab, Rajasthan, Tamil Nadu showed highest share

of wells and other irrigation. In three states viz., Tamil Nadu, Haryana and Maharashtra,

the share of wells and irrigation was more than 50% while in seven states it was between

20 and 50%.

Table 5.6: Percentage Distribution of Fixed Capital Expenditure of Households in Farm Business during 1991(I) and 2002(II)

RURAL+URBAN

Land Improvement

Orchards & Plantations

Farm Houses & etc.

Other Expenditure

STATES I II I II I II I II

Andhra Pradesh 28.24 9.42 1.18 2.47 2.75 6.73 4.31 0.45

Assam 61.5011.4

8 0.00 3.28 8.92 34.43 5.1614.7

5

Bihar 41.43 1.92 0.00 0.00 5.71 29.49 2.86 0.64

Gujarat 11.76 7.76 4.36 0.00 0.44 5.97 4.14 0.80

Haryana 1.90 0.05 0.00 0.00 35.41 5.60 1.09 0.29

Karnataka 16.6223.3

4 1.32 5.40 3.12 5.40 6.42 5.57

Kerala 34.1826.3

843.4

3 28.83 8.25 8.90 1.18 1.23Madhya Pradesh 8.27 9.63 0.24 0.00 0.36 4.82 0.61 1.33

Maharashtra 14.13 7.84 2.61 2.29 0.96 19.12 3.16 1.43

Orissa 38.6714.6

1 6.67 0.00 5.33 5.62 6.67 5.62

Punjab 1.92 2.08 0.00 0.00 1.82 9.81 4.35 0.22

Rajasthan 9.41 3.18 0.58 0.11 0.10 26.43 2.50 1.02

Tamil Nadu 16.99 4.96 0.65 4.42 4.09 9.73 1.72 4.25

Uttar Pradesh 7.23 0.47 0.21 0.00 4.89 26.36 1.70 0.47West Bengal 27.19 16.8 0.88 1.20 10.53 24.10 21.0 14.4

49

7 5 6

All India 13.74 6.88 3.17 2.56 4.02 13.92 3.81 1.92

Wells & Other Irrigation

Agricultural Machinery

Transport Equipment

Rural +Urban

I II I II I II

40.39 46.19 11.37 7.17 11.76 27.58Andhra Pradesh

0.47 8.20 3.29 19.67 20.66 8.20 Assam

14.29 4.49 21.43 26.28 14.29 37.18 Bihar

34.42 23.48 35.95 20.50 8.93 41.49 Gujarat

21.57 56.77 28.09 35.05 11.94 2.25 Haryana

33.99 35.19 2.36 15.16 36.17 9.93 Karnataka

6.90 27.30 5.22 6.60 0.84 0.77 Kerala

30.41 27.74 22.26 21.59 37.83 34.88Madhya Pradesh

53.91 52.39 11.11 11.09 14.13 5.83 Maharashtra

13.33 0.00 18.67 4.49 10.67 69.66 Orissa

6.67 39.12 37.01 31.95 48.23 16.82 Punjab

35.93 42.43 15.75 14.52 35.73 12.31 Rajasthan

37.20 63.36 34.41 11.33 4.95 1.95 Tamil Nadu

7.66 3.77 40.85 56.90 37.45 12.03 Uttar Pradesh

10.53 0.00 19.30 22.89 10.53 20.48 West Bengal

28.75 35.04 22.20 23.04 24.31 16.64 All India

As mentioned above, livestock component is also included in 2003. The shares of livestock in

total on farm capital are given in Table 5.4. It shows that the shares were more than 40% in

Assam and West Bengal and more than 20% in Andhra Pradesh, Bihar and Tamil Nadu and

between 14 and 18% in Haryana, Punjab, Orissa and Uttar Pradesh. In other words, farmers

invest considerable amount in livestock.

Table 5.4: Percentage Distribution of Livestock in total Fixed Capital Expenditure of Households in Farm Business during 2002(II)

STATES Livestock

Andhra Pradesh 22.16

Assam 44.55

Bihar 22.39

Gujarat 8.39

Haryana 17.42

Karnataka 9.89

Kerala 9.57

Madhya Pradesh 9.34

Maharashtra 9.52

50

Orissa 14.15

Punjab 14.04

Rajasthan 9.40

Tamil Nadu 20.31

Uttar Pradesh 17.16

West Bengal 44.30

All India 16.22

5.2. Agricultural Productivity, Rural Poverty and Capital Intensity Across States

It is known that agricultural land, labour productivity, rural poverty reduction and capital

intensity are related. Table 5.5 shows indicates that in general, rural poverty is lower in the states

where land and labour productivity are higher. We have estimated capital intensity for 15 states.

On farm investment for 2003 is taken from AIDIS and gross cropped area is from Ministry of

Agriculture. With few exceptions, capital intensity and agricultural productivity are correlated.

Table 5.5: State wise agricultural land productivity, labour productivity, rural poverty and capital intensityValue of output Per hectare of GCA in Rs.(land productivity)2003-06

Value of output per worker in Rs. (labour productivity)2003-06

Rural Poverty Ratio(%)2004-05

Capital Intensity in Agriculture (in Rs.), 2002

Rank land produ-ctivity

Rank labour Productivity

RankRuralpoverty

Rank Capitalintensity

Haryana 11569 14186 24.8 2835 6 3 4 2Himachal Pradesh

6176 2366 25.0 -- 12 16 5

Jammu & Kashmir

5985 3246 14.1 -- 13 15 1

Punjab 15373 30627 22.1 1367 1 1 3 5Uttar Pradesh

9894 6388 42.7 1271 8 7 12 6

North-West Region

10958 8565 - - - - - -

Assam 8989 6956 36.4 127 9 6 8 15Bihar 5670 1697 55.7 261 15 17 16 14Orissa 6690 4420 60.8 265 11 14 17 13West 12142 7642 38.2 360 4 5 11 11

51

BengalEastern Region

8314 3913 - -- -- -- -- --

Gujarat 11836 9833 39.1 826 5 4 13 8Madhya Pradesh

5640 4973 53.6 312 16 13 15 12

Maharashtra 5960 4998 47.9 773 14 12 14 9Rajasthan 5095 6093 35.8 1967 17 9 7 3Central Region

6367 5917 - -- -- -- -- --

Andhra Pradesh

11537 6091 32.30 874 7 10 6 7

Karnataka 6994 6175 37.5 572 10 8 10 10Kerala 13858 17034 20.2 3401 2 2 2 1Tamil Nadu 13117 4910 37.5 1730 3 11 9 4Southern Region

10244 6220 - - - - - -

All India 8460 4949 41.8 952 - -

Source: Bhalla and Singh (2009) for agricultural productivity and Planning Commission (2009) for rural poverty.

Table 5.6 divides states into three categories viz., top 5 states, middle 5 states, bottom 5 states 15.

In the top 5 states, three states viz., Punjab, Kerala, Haryana are common in capital, labour

productivity and rural poverty. Tamil Nadu, West Bengal, Gujarat and Rajasthan are common in

at least two categories. States are also common in most of the cases in middle five states. In the

bottom five states, Madhya Pradesh, Orissa and Bihar are least capital intensive with least

agricultural productivity and high rural poverty. Maharastra is common in land and labour

productivities and rural poverty. The analysis shows that capital intensity increases land and

labour productivities which in turn reduces rural poverty. It shows the importance of farm

investment for reducing poverty.

Table 5.6. Classification of States based on capital intensity, land and labour productivity, and Rural Poverty Capital Intensity Land Productivity Labour Productivity Rural Poverty (in

ascending order)Top 5 states Top 5 states Top 5 states Top 5 statesKeralaHaryanaTamil NaduRajasthanPunjab

PunjabKeralaTamil NaduWest BengalGujarat

PunjabKeralaHaryanaGujaratWest Bengal

KeralaPunjabHaryanaAndhra PradeshRajasthan

Middle Five States Middle Five States Middle Five States Middle Five StatesUttar PradeshAndhra PradeshGujarat

HaryanaAndhra PradeshUttar Pradesh

AssamUttar PradeshKarnataka

AssamTamil NaduKarnataka

15 We have not included here J&K and Himachal Pradesh

52

MaharashtraKarnataka

AssamKarnataka

RajasthanAndhra Pradesh

West BengalUttar Pradesh

Bottom 5 states Bottom 5 States Bottom 5 states Bottom 5 statesWest BengalMadhya PradeshOrissaBiharAssam

OrissaMaharashtraBiharMadhya PradeshRajasthan

Tamil NaduMaharashtraMadhya PradeshOrissaBihar

GujaratMaharashtraMadhya PradeshBiharOrissa

5.3. Term Credit

Another way of looking at farm investment is to look at term credit. Table 5.7 provides the sub-

sector wise ground level term credit flow for agriculture & allied activities for 2001-02 and

2009-10. Term credit is mostly used for investment purposes. In 2001-02, ‘others’ category has a

share of 45% and the details are not known. Next farm mechanization has the highest share with

18% followed by hi-tech agriculture (10.5%) and animal husbandry(10.3%). Minor irrigation has

a share of 8.6% in 2001-02. In the year 2009-10, the share of hi-tech agriculture increased

significantly to 43.5% from 10.5% in 2001-02. The shares of land development and plantation

and horticulture also increased. It looks like farmers are spending more on horticulture now. The

shareof minor irrigation and farm mechanization declined. Animal husbandry’s share is almost

same in 2009-10. In nominal terms, farmers invested Rs.10,000 crores on animal husbandry,

Rs.43,900 crores on hi-tech agriculture, Rs.10,200 crores on farmechanization and Rs.6,400

crores on plantation and horticulture.

Table 5.7. Sub-Sector wise Ground level Term credit flow for agriculture & allied activitiesMajor Sub-Sector Term Credit (Rs. Crores) Shares of sub-sectors in total term credit (%)

2001-02 2009-10 2001-02 2009-10Minor irrigation 1845 5197 8.6 5.1Land development 307 3669 1.4 3.6Farm mechanization 3847 10211 17.9 10.1Plantation and horticulture

765 6407 3.6 6.3

Animal Husbandry 2221 10260 10.3 10.2Fisheries 508 1854 2.4 1.8Hi tech agriculture 2257 43904 10.5 43.5Others 9786 19463 45.4 19.3Total Term Credit 21536 100965 100.0 100.0

Source: NABARD (2011), personal communication

53

SECTION 6CAPITAL FORMATION IN AGRICULTURE USING FARM

LEVEL DATA (COST OF CULTIVATION DATA)

In this section, we examine trends in growth and composition of capital stock using the farm

level data using the cost of cultivation studies. The Directorate of Economics and Statistics

(DES), Ministry of Agriculture, Government of India, has instituted a scheme called

“Comprehensive Cost of Cultivation Scheme” (CCCS), under which farm level data are

collected by Agricultural Universities and other Research Institutions spread across the Country.

Data are collected on all Principal Crops for different seasons and years by field level technical

staff of DES from over 8000 sample farmers spread across the agro – climatic regions of the

Country. The collection of data is through 39 schedules (or record types) following cost

accounting method. Depending on nature of data, the field level technical staff of the CCCS

collects data from sample farmers on daily, monthly or yearly basis. The results on cost of

cultivation derived from data, are provided to Commission on Agricultural Costs and Prices

(CACP), Government of India so as to facilitate recommendations on Minimum Support Prices

(MSPs) on all Principal Crops. These recommendations are the basis for Government of India to

announce MSPs for crops during two major seasons.

The first data set is aggregative in nature in the sense that it consists of values of capital assets

such as land capital, animal capital, irrigation capital and farm machinery, and data are drawn

from the general data pool of DES, collected from 15 States for the years 1994-95 and 2007-08.

Given the nature of the data accessible in aggregative form, three ratios have been computed:

• Animal Capital/Farm Machinery Ratio (AC/FM)

• Farm Machinery/Irrigation Capital Ratio (FM/IC)

• Animal Capital/Irrigation Capital Ratio (AC/IC).

Growth Rates of AC, FM and IC have been computed. Compositional shifts have been analysed

in two ways:

Percentage value share of land, AC, FM and IC in total capital stock.

54

Percentage value share of non-land capital assets viz AC, FM and IC in the total

of non-land capital assets.

The ratio, growth rate and compositional shift analyses have been done for two periods of time

viz 1994-95 and 2007-08, forming a total of 13 years adequate enough to identify temporal

shifts.

6.1. Growth Rates of Capital Assets

We have considered here capital assets including land and excluding land values. These assets

also exclude buildings as they are not exclusively meant for farming.

Table 6.1 provides growth rates for land and non-land assets. The following inferences can be

drawn from the table.

(a) At all India level, land capital growth was more than 3 per cent per annum while non-

land growth of capital assets was only 0.72 per cent per annum. Among non-land capital,

the growth rates of animal capital (-0.74%) is found to be negative, while the growth rate

of irrigation capital (1.16) and non-irrigation machinery (1.93) are found to be positive.

(b) At the state level, eight out of 15 states showed positive growth of non-land capital assets

while 13 out of 15 states recorded positive growth if we include land in the capital assets.

It shows that land values have increased significantly over time in many states.

(c) In the non-land assets, highest growth rate was recorded by Tamil Nadu, followed by

Maharashtra, Rajasthan, Himachal Pradesh, Haryana, Bihar and Punjab.

(d) In irrigation machinery, six states recorded positive growth while 11 out of 15 states

showed positive growth for non-irrigation machinery.

Table 6.1.Growth Rates (without Building): 1994-95 to 2007-08 (13 years)

State Land AnimalIrrigation Machinery

Non-irrigation Machinery

Total Capital C (excludes land)

Total Capital K(includes land)

Andhra Pradesh 7.23 -1.5 -5.9 -1.3 -3.33 6.61

Bihar 0.37 -5.5 -2.4 9.23 1.49 0.4

Gujarat 6.48 -3 -3.6 3.12 -1.71 5.48

Haryana 7.94 1.7 4.09 0.45 1.68 7.7

Himachal Pradesh 5.72 0.13 5 3.71 2.28 5.56

Karnataka -0.26 -2.6 -2 -3.5 -2.73 -0.4

Kerala -0.94 -5.7 -12 1.15 -6.45 -1

55

Maharashtra 6.44 1.56 2.67 13.3 4.41 6.26

Madhya Pradesh 1.57 -3.3 -1.4 4.67 -0.05 1.45

Orissa 0.47 -0.5 -8.1 3.13 0.81 0.48

Punjab 3.15 0.9 7.74 -1.1 1.3 3.06

Rajasthan 2.86 -0.4 1.76 6.59 2.35 2.8

Tamil Nadu 6.15 -0.5 6.08 4.57 4.55 6.04

Uttar Pradesh 1.46 -3.6 -1.1 -0.1 -1.24 1.27

West Bengal 1.55 -3.8 -1.6 1.42 -1.93 1.39Aggregate level 3.75 -1.4 1.16 1.93 0.72 3.58

K = Land + C C = Animal + Irrigation Machinery + Non-irrigation Machineryable

6.2. Composition of Capital Assets

Changes in composition also show similar trends. At the all India level, the share of non-

irrigation machinery and irrigation machinery improved while that of animal capital declined in

the total non-land capital assets (see Table 6.2 and Fig 6.1.). If we consider land as component of

total capital assets, the share of land was more than 90% and increased over time. Also, with

inclusion, the shares of non-land capital assets declined drastically and they were less 2% in

2007-08 (Table 6.3 and Fig 6.2)

6.2. Composition of non-land capital assets (without Building)

Animal Irrigation Machinery Non-irrigation Machinery

Aggregate level 1994-95 2007-08 1994-95 2007-08 1994-95 2007-08

C 0.32 0.25 0.35 0.37 0.33 0.39

Fig 6.1. Composition of non-land capital assets

56

6.3. Composition of total capital (without Building) Land Animal Irrigation Machinery Non-irrigation Machinery

Aggregate level 1994-95 2007-08 1994-95 2007-08 1994-95 2007-08 1994-95 2007-08

K 0.934 0.954 0.021 0.011 0.023 0.017 0.022 0.018

Fig 6.3. Composition of total capital assets

Table 6.4 provides compositional changes in total non-land capital assets. The inferences from

the table are the following.

(a) The share of animal capital increased only in two states viz., Andhra Pradesh and Kerala.(b) In the case of irrigation machinery, six states recorded increase in their shares – highest

increase being in Punjab(c) The share of non-irrigation machinery increased in all states except in Karnataka.(d) Regarding levels of shares in 2007-08, the shares of animal capital are more than 35% in

Andhra Pradesh (0.39%), Himachal Pradesh (38%), Karnataka (47%), Orissa (55%) and West Bengal (46%).

(e) The share of irrigation machinery was more than 40% in Gujarat (41%), Maharashtra (46%), Rajasthan (56%) and Tamil Nadu (64%) in 2007-08.

(f) Similarly, the share of non-irrigation machinery was more than 40% in seven states viz., Bihar (68%), Haryana (42%), Himachal Pradesh (49%), Madhya Pradesh (42%), Orissa (45%), Punjab (46%), and Uttar Pradesh (63%).

Regarding the composition of capital assets including land, the share of land is more than 90% in

all states except in Rajasthan.

Table 6.4.Composition of capital stock excluding land (without Building)

57

State Animal capital Irrigation Machinery Non-irrigation machinery

1994-95 2007-08 1994-95 2007-08 1994-95 2007-08

Andhra Pradesh 0.3 0.39 0.5 0.35 0.2 0.26

Bihar 0.6 0.24 0.14 0.08 0.26 0.68

Gujarat 0.29 0.24 0.52 0.41 0.19 0.35

Haryana 0.3 0.3 0.2 0.28 0.5 0.42

Himachal Pradesh 0.5 0.38 0.1 0.14 0.41 0.49

Karnataka 0.46 0.47 0.22 0.24 0.32 0.29

Kerala 0.45 0.5 0.44 0.19 0.11 0.31

Maharashtra 0.32 0.22 0.57 0.46 0.11 0.32

Madhya Pradesh 0.33 0.22 0.44 0.37 0.23 0.42

Orissa 0.65 0.55 0.02 0.01 0.33 0.45

Punjab 0.22 0.21 0.15 0.33 0.63 0.46

Rajasthan 0.25 0.18 0.58 0.53 0.17 0.29

Tamil Nadu 0.23 0.12 0.53 0.64 0.24 0.24

Uttar Pradesh 0.32 0.23 0.14 0.14 0.54 0.63

West Bengal 0.59 0.46 0.19 0.2 0.22 0.34

Aggregate level 0.32 0.25 0.35 0.37 0.33 0.39 Table 6.5.Composition of total capital including land (without Building)

State Land Animal Capital Irrigation machinery Non-irrigation machinery

1994-95 2007-08 1994-95 2007-08 1994-95 2007-08 1994-95 2007-08

Andhra Pradesh 0.903 0.973 0.03 0.011 0.048 0.009 0.019 0.007

Bihar 0.973 0.969 0.016 0.007 0.004 0.003 0.007 0.021

Gujarat 0.822 0.929 0.051 0.017 0.093 0.029 0.034 0.025

Haryana 0.947 0.975 0.016 0.008 0.011 0.007 0.026 0.011

Himachal Pradesh 0.944 0.963 0.028 0.014 0.005 0.005 0.023 0.018

Karnataka 0.933 0.951 0.031 0.023 0.015 0.012 0.022 0.014

Kerala 0.993 0.997 0.003 0.002 0.003 7E-04 8E-04 0.001

Maharashtra 0.903 0.923 0.031 0.017 0.056 0.036 0.011 0.024

Madhya Pradesh 0.917 0.932 0.028 0.015 0.036 0.025 0.019 0.028

Orissa 0.954 0.952 0.03 0.026 8E-04 3E-04 0.015 0.022

Punjab 0.947 0.958 0.012 0.009 0.008 0.014 0.033 0.019

Rajasthan 0.883 0.889 0.03 0.02 0.068 0.059 0.02 0.032

Tamil Nadu 0.929 0.941 0.016 0.007 0.038 0.038 0.017 0.014

Uttar Pradesh 0.919 0.942 0.026 0.014 0.011 0.008 0.044 0.036

West Bengal 0.946 0.965 0.032 0.016 0.01 0.007 0.012 0.012

Aggregate level 0.934 0.954 0.021 0.011 0.023 0.017 0.022 0.018

Regarding ratios of different capital, the share of animal/irrigation machinery declined in all

states except in Haryana, Karnataka and Punjab (Table 6.6 and fig 6.4). In contrast, the ratio of

non-irrigation machinery to irrigation machinery increased in all states except in Haryana,

58

Himachal Pradesh, Karnataka, Punjab and Tamil Nadu (Table 6.7 and fig 6.5). It shows that

importance of non-irrigation machinery has been increasing in the total non-land assets in most

parts of India in recent years.

Table 6.6 Animal / Non-irrigation Machinery

State 1994-95 2007-08

Andhra Pradesh 1.52 1.47

Bihar 2.31 0.35

Gujarat 1.52 0.68

Haryana 0.61 0.71

Himachal Pradesh 1.22 0.77

Karnataka 1.42 1.6

Kerala 3.98 1.6

Maharashtra 2.9 0.7

Madhya Pradesh 1.46 0.52

Orissa 1.95 1.22

Punjab 0.36 0.47

Rajasthan 1.51 0.62

Tamil Nadu 0.97 0.5

Uttar Pradesh 0.6 0.38

West Bengal 2.68 1.36Aggregate level 0.97 0.64

Fig 6.4. Ratio of Animal/non-irrigation machinery

Table 6.7.Non-irrigation Machinery / Irrigation Machinery

59

State 1994-95 2007-08

Andhra Pradesh 0.4 0.76

Bihar 1.9 8.2

Gujarat 0.36 0.87

Haryana 2.44 1.54

Himachal Pradesh 4.22 3.6

Karnataka 1.49 1.22

Kerala 0.26 1.64

Maharashtra 0.19 0.69

Madhya Pradesh 0.52 1.14

Orissa 19.4 86.5

Punjab 4.25 1.39

Rajasthan 0.29 0.54

Tamil Nadu 0.45 0.37

Uttar Pradesh 3.92 4.47

West Bengal 1.13 1.69Aggregate level 0.95 1.04

Fig 6.5. Ratio of non-irrigation machinery/irrigation machinery

60

SECTION 7

DETERMINANTS OF OUTPUT, PRODUCTIVITY AND CAPITAL FORMATION: FARM LEVEL DATA

In this section, we examine the determinants of farm investments using the farm level data for

three selected states i.e. Punjab, Andhra Pradesh and Orissa for the years 1994-95 and 2007-0816.

The data set on farm households have been developed, drawing data from the general data pool

of DES. In the present study, three States based on the criterion of productivity of land have been

sampled – High Productivity State (Punjab), Medium Productivity State (Andhra Pradesh) and

Low Productivity State (Orissa). From each one of these three sample States, 50% of the samples

of DES study for the years 1994-95 and 2007-08 have been selected at random, the randomness

being every alternative sample from each Tehsil. Out of the total sample of 2700 for these three

states, we have 1350 sample data for the present analysis. Data from these sample farm

households on educational level, operated land holding (both area and value), area under

irrigation, values of Animal Capital (AC), Farm Machinery (FM) and Irrigation Capital (IC),

total labour (both family and hired) employed for farm operations, gross value of output and

credit facility availed have been drawn from the general data pool of Directorate of Economics

and Statistics (DES). The capital data is converted into constant prices by using the deflators

from CSO. Land value is also included in the capital stock.

7.1. Regression Models for Identifying the Determinants of output, productivity and capital

Here we examine the factors that determine gross value of output, labour productivity and capital

stock. To address the issues lined up above, three basic regression models are proposed viz GVO

function, labour productivity function, and capital formation function. It needs to be stated that

the choice of variables as well as their measurement depends on the accessibility to farm level

data.

The definition and measurement of dependent and independent variables used for both

aggregative and disaggregative analysis are given below.

16 The regression results and analysis are borrowed from joint work. See Bisaliah et al (2011)

61

GVO = Gross value of farm level output, measured in `, and deflated using data on AGDP at current and constant prices reported in National Income Accounts of Central Statistical Organisation (CSO).

AC = Value of animal capital measured in `, and deflated using the deflator developed by CSO.

FM = Value of farm machinery measured in `, and deflated using the deflator developed by CSO.

IC = Value of irrigation capital measured in `, and deflated using the deflator developed by CSO.

NLC = Non-land capital (AC+FM+IC) measured in `, and deflated using the deflator developed by CSO.

A = Area of land measured in hectare.

L = Labour (total of both family and hired/casual labour) measured in physical units of man days.

DVL = Literacy status expressed in the form of dummy variable (DVL) with a value of 1 if the farmer is literate and zero otherwise.

DVCR = Credit status expressed in the form of dummy variable (DVCR) with a value of 1 if credit availed by the farmer and zero otherwise.

DVY = Year dummy variable (DVY) with a value of 1 for 2007-08, and 1994-95 is the base with a value of zero.

DVS1 = State dummy variable (DVS1) with a value of 1 for Andhra Pradesh and zero for Punjab, where the data of these two States are pooled.

DVS2 = State dummy variables (DVS2) for the pooled data analysis of all the three States.

GVO/L = GVO per physical unit of labour (i.e productivity of labour). GVO and L as defined and measured earlier.

AC/A = Value of animal capital per hectare measured in `.

FM/A = Value of farm machinery per hectare, measured in `.

DVIR = Irrigation dummy variable (DVIR) with a value of 1 for presence of irrigation and zero otherwise.

62

Three regression functions are specified below. It may be noted that all these

regression functions are specified in log form and relevant elasticities are derived

from the estimated functions.

GVO Regression Function:

Depending on availability/accessibility to farm level data, the major determinants of GVO

identified are: Animal Capital (AC), Farm Machinery (FM), Land Area (A), Irrigation (DVIR),

Literacy (DVL), Credit Availment (DVCR) and year effect and regional effect/State effect, to be

captured through dummy variables. Given these determinants, a complete GVO function in

logarithmic form (Ln) is specified as follows:

Ln GVO = A+a1 LnAC+a2 LnFM+a3LnA

+a4 DVIR+a5 DVL+a6DVCR

+a7DVY+a8DVS1+u ………… (1)

Where: A is the constant term, as are the regression coefficients/GVO

elasticities, and u is the random error term.

Labour Productivity Function:

The major determinants of labour productivity at farm level are postulated to be: animal capital

per hectare of land (AC/A), farm machinery per hectare of land (FM/A), irrigation (DVIR),

literacy (DVL), credit availment (DVCR), and year effect (DVY) and regional/State effects

(DVS1/DVS2). A complete model of labour productivity is specified as detailed below:

Ln (GVO/L) = B+b1Ln AC/A+b2Ln FM/A+b3 DVIR+b4 DVL+b5 DVCR+b6 DVY+b7

DVS+u………… (2)

Where: B is the constant term, bs are regression coefficients/elasticities and u is the random error

term.

63

Capital Formation Function:

The determinants of non-land capital assets are land (A), credit (DVCR), literacy (DVL), year effect (DVY) and state effect (DVS).

LnNLC= C+C1LnA+C2DVCR+C3DVL+C4DVY+C5DVS+u……..(3)

Where: C is the constant term, Cs are regression coefficients/elasticities and u is the random error

term.

Analysis on DeterminantsWe have done the following four types of analysis for determinants of gross value added, productivity and capital formation.

a. Aggregate of all farms for all three sample states (Punjab, Orissa, Andhra Pradesh) together and individual states separately

b. Aggregate of irrigated farms for Punjab and Andhra Pradesh and rainfed farms in Andhra Pradesh

c. Farm wise (marginal and small, semi medium and medium and large farms) for all three sample states (Punjab, Orissa, Andhra Pradesh) together and individual states separately

d. Farm wise (marginal and small, semi medium and medium and large farms) for irrigated farms of Punjab and Andhra Pradesh and rainfed farms in Andhra Pradesh

The results are given below.

7.2. Results for three sample states and individual states at aggregate level

7.2.1.Gross Value of Output Function Estimates:

The results derived with pooled data analysis of Andhra Pradesh and Punjab (Table 7.1) suggest

that:

• Animal capital, farm machinery, land, labour, literacy, and credit availment-all have positive and significant impact on GVO.

• The explanatory power of the estimated model is validated by high Adj. R2 of 71%.

State-wise elasticities of GVO are presented in Table 7.2.

• With respect to Punjab:

(a) Animal capital, farm machinery, land, labour and credit availment are found to have positive and significant impact on GVO. The literacy variable, even though insignificant, has positive influence on GVO.

(b) High Adj. R2 of 90% validates the explanatory power of the estimated model.

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• With respect to Andhra Pradesh:

(a) Elasticities of output with respect to animal capital, land, labour, literacy and credit availment are all positive and significant. Elasticity of output with respect to farm machinery, although insignificant, is positive.

(b) Adj. R2 of 63% supports the explanatory power of the independent variables included in the model.

• With respect to Orissa:

(a) Output elasticities of farm machinery and labour are positive and significant. But regression coefficients of animal capital and literacy, even though negative, are not significant.

(b) Due to ‘inadequacy’ of data, the value of Adj. R2 is low at 43%.

7.2.2. Labour Productivity Function Estimates:

The values of estimated coefficients derived with the pooled data of Andhra Pradesh and Punjab

for labour productivity function are presented in Table 7.3.

• Animal capital, farm machinery, literacy and credit availment are all found to have positive and significant impact on labour productivity.

• The Adj. R2 of 38%, even though low, appears to support the validity of the model.

7.2.3.Capital Formation Function Estimates:

From the results on capital formation function (Table 7.4) with pooled data analysis of Punjab

and Andhra Pradesh sample farmers, two observations could be made:

• The coefficient of land, credit availment, and literacy are positive as well as statistically significant.

• The Adj. R2 of 0.34 suggests that the independent variables could explain only 34% of variation in capital formation at the farm level.

7.3. Results for Aggregare Irrigated Farms in Andhra Pradesh and Punjab and rainfed in Andhra Pradesh

65

7.3.1.Determinants of GVO:

The estimated elasticities (regression coefficients) of GVO with respect to

animal capital, farm machinery, irrigation capital, land, labour, literacy, and

credit availment are in Tables 7.5 and 7.6:

• The estimated GVO function, using pooled data of 831 samples of irrigated farms of Punjab and Andhra Pradesh (Table 7.5), validates the inference that 71% of variation (Adj. R2 = 0.71) in GVO is explained by the explanatory variables specified in the model. The three broad results are:

• The elasticities of GVO with respect to labour, land, credit availment, farmer literacy, and farm machinery are significant, with a t-value of more than one.

• The surprising result is negative and significant elasticity of GVO with respect to irrigation capital.

• Even though the elasticity of GVO with respect to animal capital is positive, it is not statistically significant.

7.3.2. The State-wise estimates of GVO function are in Table 7.6:

• In case of Punjab, the high Adj. R2 of 0.86 validates the model in the sense that 86% of variations in GVO are explained by the explanatory variables included viz farm machinery, irrigation capital, land, labour, literacy and credit availment. The coefficients/output elasticities are not only positive, but also statistically significant. The elasticity of GVO with respect to animal capital is positive, but not statically significant.

• The explanatory power of the estimated GVO model for Andhra Pradesh is quite satisfactory with Adj. R2 of 0.64. Animal capital, land, labour, literacy and credit availment are found to have positive and significant influence on GVO, with a t-value of more than one. But the surprising result is negative and statically significant elasticity of GVO with respect to irrigation capital. With respect to farm machinery, elasticity coefficient is negative, but statistically insignificant.

7.3.3.Labour Productivity Function Estimates:

66

The aggregative and disaggregative (State-wise) labour productivity

functions are estimated with animal capital per hectare, farm machinery per

hectare, irrigation capital per hectare, literacy and credit availment as

explanatory variables. The empirical results are in Tables 7.7 and 7.8:

• In case of pooled data analysis of Punjab and Andhra Pradesh (Table 7.7), only coefficients of literacy and credit availment are both positive and statistically significant; the coefficient of farm machinery is positive, but not statistically significant and the coefficient of animal capital is negative, but not statically significant. The coefficient of irrigation capital is negative, but highly significant. Adj. R2 of 36% is not high enough to validate the explanatory power of the model.

• With respect to State-wise estimates (Table 7.8) of labour productivity functions, Adj. R2 values are very low; but in case of Punjab, farm machinery, irrigation capital, literacy and credit availment are found to have statically positive impact on labour productivity. In case of irrigated farms of Andhra Pradesh, animal capital, literacy and credit availment are found to have positive and significant impact on labour productivity, whereas farm machinery and irrigation capital have negative and significant impact.

7.3.4.Estimated Capital Formation Functions:

Tables 7.9 and 7.10 present results on determinants of capital formation in

irrigated sample farms.

• It is seen from Table 7.9 that only land and credit availment are statistically significant to have positive impact on capital formation.

• As could be seen from the State-wise disaggregative analysis presented in Table 4.6, land, credit availment and literacy are found to have statically significant positive impact on capital formation in Punjab, whereas in Andhra Pradesh only the coefficient of land is both positive and statically significant, and coefficient of credit availment even though positive, is not statically significant.

7.3.5 Estimated gross value of output function of irrigated and rainfed farms for Andhra

Pradesh

Estimates of GVO elasticities of irrigated and rainfed farms are in Table 7.11.

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• With regard to irrigated farms, animal capital, land, labour, literacy and credit availment are found to have positive and statistically significant influence on GVO. The explanatory power of the estimated GVO function is sound enough with an Adj. R2 of 64%. But the elasticity of GVO with respect to irrigation capital is negative as well as highly significant. In case of rainfed farms, the estimated GVO function (following bootstrapping Davison, et. al) suggests that farm machinery, land, labour, literacy and credit availment are significant variables to have positive influence on GVO. Adj R2 is also moderately high (40%), validating the explanatory power of the estimated model.

7.4. Results for Farm-wise (marginal and small, semi-medium and medium and large)for all three sample states together and individual states

7.4.1. Estimated Gross Value of Output Function

On determinants of GVO of three farm groups, the analysis is performed with the pooled data of

these farms. The State – wise results on GVO of these farm groups are not reported in view of

low explanatory power of the estimated GVO functions. It is observed from Table 7.12 that:

• In case of marginal and small farms, farm machinery, labour and literacy are found to have positive and significant impact on GVO. The regression coefficients of animal capital and land, even though negative, are not significant, and of credit availment, even though insignificant, is positive. The relative high Adj. R2 of 59% validates the explanatory power of the model estimated.

• In case of semi – medium and medium farms, the output elasticities of farm machinery, labour and credit availment are significant and positive. The regression coefficients of animal capital and literacy, even though insignificant, are positive.

• In case of large farms, the output elasticities of farm machinery, land, labour, and literacy are both positive and statistically significant. Regression coefficient of animal capital, even though negative is insignificant, and of credit availment, even though positive, is not significant. Adj. R2 of 83% validates the explanatory power of the model estimated.

7.4.2. Estimated Labour Productivity Function

On determinants of labour productivity, the results on only farm group-wise are reported, and

those on State-wise are not reported in view of inadequate explanatory power of the model. As

shown in Table 7.13.

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• In case of marginal and small farms, the coefficients of animal capital, farm machinery, and literacy are not only significant, but are also found to have positive impact on labour productivity. Credit availment, even though insignificant, is found to have positive impact on labour productivity in marginal and small farms.

• With regard to semi – medium and medium farms, the coefficients of farm machinery, literacy and credit availment are both positive and significant. The coefficient of animal capital, even though insignificant, is positive.

• In case of large farms, the elasticities of labour productivity with respect to farm machinery and literacy are found to be positive and almost significant. But the negative coefficient of animal capital is disturbing, and it is almost significant. The coefficient of credit availment variable, even though insignificant, is found to have positive impact on labour productivity.

7.4.3. Estimated Capital Formation FunctionThe results on determinants of capital formation are in Table 7.14

• In case of marginal and small farms, the coefficients of land, credit availment, and literacy are positive as well as statistically significant.

• With regard to semi – medium and medium farms, the efficient of land alone is both positive and significant. The coefficient of credit availment is positive, but insignificant, and that of literacy negative but insignificant.

• In case of large farms, credit availment alone is found to be both positive and significant. The coefficients of literacy and land are both positive, but statistically insignificant.

7.5. Farm Groups results for Irrigated farms in Andhra Pradesh and Rainfed Farms in

Andhra Pradesh

Irrigated Farms in Punjab and Andhra Pradesh

7.51Estimates of Gross Value of Output:

The results on farm group analysis are in Table 7.15:

• In case of marginal and small farms, GVO elasticities with respect to land, labour, literacy and credit availment are positive as well as statistically significant. But elasticity of irrigation capital is significant, but the sign is negative and thereby not in accordance with the postulated sign. The elasticities of other two capital assets viz animal capital and

69

farm machinery are not statistically significant. Adj. R2 of 0.64 is high enough to validate the estimated function, in terms of its explanatory power.

• With regard to semi-medium and medium farms, animal capital, farm machinery, labour, literacy and credit availment have positive elasticities, somewhat statistically significant with t-values of more than one. But the elasticity with respect to irrigation capital is negative, and statically significant. Adj. R2 of 0.58 is adequate enough to vindicate the validity of estimated GVO function.

• In respect to large farms, the elasticities of GVO with respect to farm machinery, land, labour and literacy are found to be positive, and statistically significant with t-values of more than one. But elasticities with respect to irrigation capital and animal capital are negative, and statistically significant with t-values of more than one. The elasticity with respect to credit availment is positive, but not statistically significant. With a high Adj. R2

of 85%, the explanatory power of the model is validated.

7.5.2. Labour Productivity Function Estimates:

The estimates on labour productivity functions for three farm groups are in Table 7.16. In case of

marginal and small farmers only two elasticities viz literacy and credit availment are found to

have positive and significant impact on labour productivity and in case of three capital

components unexpected negative impact is observed.

• With regard to labour productivity function of semi-medium and medium farms, the coefficient of farm machinery, literacy and credit availment are both positive and statistically significant. The coefficient of irrigation capital is both negative and significant.

• In case of large farms, the coefficients of farm machinery and literacy are positive as well as significant. But coefficients of animal capital and irrigation capital are negative as well as significant.

• Adj. R2 for labour productivity functions of marginal and small, and semi-medium and medium farms is just moderate, and in case of large farms, it is found to be 69%.

7.5.3. Estimates of Capital Formation Function:

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The farm group-wise capital formation estimated functions are in Table 7.17. Land has turned to

be both a positive and significant variable in influencing capital formation in all the three farm

groups. Credit availment has both positive and statistically significant impact on capital

formation in large farms, and in case of other two farm groups credit availment has positive

impact but not statically significant. Literacy variable is found to have positive impact on capital

formation in case of marginal and small farms and large farms, where as its impact is negative

with respect to semi-medium and medium farms

7.6. Farm Group wise Results for Irrigated and Rainfed Farms of Andhra Pradesh

Table 7.18 provides estimated GVO functions for three farm groups of irrigated farms.

• It is observed that land, labour, literacy and credit availment have positive significant influence on GVO of marginal and small farms. In case of semi-medium and medium irrigated farms, animal capital, labour and literacy are found to have positive and significant influence. Credit availment and animal capital have positive influence on GVO, but statistically insignificant. But the elasticity of GVO with respect to irrigation capital is both negative and highly significant. Coefficients of farm machinery, labour, and literacy are positive as well as significant in case of large irrigated farms. Land is found to have positive influence on GVO of large irrigated farms, but the coefficient is statically insignificant.

Table 7.19 provides results for rainfed farms in Andhra Pradesh.

• Even though number of samples of rainfed farms is small for marginal and small farms and semi-medium and medium farms, the estimated GVO function (following boot strapping method) results are considered for comparison with those of irrigated farms. It could be observed from Table 7.19 that farm machinery, land and labour are major determinants of GVO of marginal and small farms under rainfed farming, and the coefficients of animal capital, literacy and credit availment, even though positive, are not statistically significant. In case of semi-medium and medium rainfed farms, labour, literacy and credit availment are found to have significant positive influence on GVO, and the coefficients of farm machinery and land, even though positive, are not statistically significant. However low Adj. R2 of the estimated equations does not validate adequately the explanatory power of the model.

• Empirical results on labour productivity functions and on capital formation functions for comparison are not reported, in view of extremely low Adj. R2.

71

Based on the determinants policies can be analysed. Government has to have policies to induce

on farm investment like irrigation capital, farm machinery, animal capital, land value.

One of the important policies relate to governance e.g. property rights and law and order. Second

one is facilitating development of rural financial institutions to have savings, insurance and

credit.

Price policy can play important role in private investment. But, clearly, there were some severe

structural factors that negated the relative price advantage and constrained investment in the

agriculture sector. It is evident that in irrigation, private investment has invariably gone for

expanding the area under pump irrigation, using groundwater resources. Unfortunately, this has

not been accompanied by sufficient public investment in recharging the aquifers and maintaining

the underground reservoir.

This could not be expected from the private sector. With a dramatic drop in water tables and a

resultant hike in irrigation costs, and especially in the context of highly erratic electricity

supplies which make farmers dependent on diesel, private investment in even this form of

irrigation has not increased. Moreover, this has made a complete hash of the irrigation regime

that has been adopted since the Green Revolution.

Instead, a properly working private-public partnership could have resulted in achieving

sustainable and more inclusive irrigation practices, based on regulated utilisation of continually

recharged and sustainable groundwater resources, eminently possible given the annual rainfall.

Public investment in roads, electricity, marketing etc. can also improve on-farm investment.

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TablesFor Section 7

73

Results for pooled data of sample three states (Punjan, Andhra Pradesh and Orissa) and Individual States

Table 7.1.: Elasticities of GVO: Analysis of Results from Pooled Data of Andhra Pradesh and Punjab.

VariablesRegression Coefficients/

Elasticitiest – Value

Constant (A) 5.41 22.79

AC 0.016 2.13

FM 0.024 1.66

A 0.216 5.45

L 1.008 23.69

DVL 0.161 2.83

DVIR -0.352 -6.25

DVCR 0.165 3.03

DVY 0.325 6.87

DVS1 -0.811 -2.14

Adj. R2 = 0.71 Number of Samples: 900

Table 7.2: Elasticities of GVO: Analysis of Results from Individual States

VariablesRegression Coefficients

Punjab Andhra Pradesh Orissa

Constant (A) 8.712 (29.78) 4.234 (14.78) 0.666 (1.13)

AC 0.015 (1.2) 0.020 (2.13) -0.039 (-0.78)

FM 0.041 (2.78) 0.007 (0.34) 0.172 (2.08)

A 0.759 (16.24) 0.142 (2.76) -0.72 (-4.61)

L 0.287 (5.26) 1.107 (21.06) 1.651 (10.27)

DVL 0.039 (0.87) 0.177 (2.2) -0.205 (-0.77)

DVIR -0.096 (-1.34) -0.386 (-5.52) NA

DVCR 0.055 (1.38) 0.143 (1.71) NA

DVY 0.474 (11.66) 0.274 (4.19) -0.036 (-0.23)

Adj. R2 0.90 0.63 0.43

Note: t-Values in Parenthesis : Number of Samples:

Punjab : 300Andhra Pradesh : 600Orissa : 443

74

Table 7.3.: Results on Labour Productivity Function: Pooled Data Analysis of Punjab and Andhra Pradesh

VariableRegression Coefficients/


Constant (B) 5.73 45.2

AC/A 0.007 1.01

FM/A 0.021 1.63

DVIR -0.202 -4.26

DVL 0.118 2.41

DVCR 0.192 4.08

DVY 0.311 7.72

DVS (Andhra Pradesh) -0.801 -14.17

Adj. R2 0.38

Note: Number of Samples: 900

Table 7.4: Results on Capital Formation Function: Pooled Data Analysis of Punjab and Andhra Pradesh



Constant (C) 10.35 54.48

A 0.967 14.19

DVCR 0.325 2.38

DVL 0.163 1.12

DVY -0.263 -2.24


Adj. R2 0.34

Note: Number of Samples: 900.

75

Results for Aggregare Irrigated Farms in Andhra Pradesh and Punjab and rainfed in Andhra Pradesh

Table 7.5.: Elasticities of GVO: Analysis of Results from Pooled Data of Andhra Pradesh and Punjab

VariablesRegression Coefficients/


Constant (A) 6.011 25.32

AC 0.007 0.94

FM 0.016 1.09

IC -0.032 -5.76

A 0.271 6.95

L 0.940 22.39

DVL 0.173 3.13

DVCR 0.235 4.63

DVS1 -0.789 -12.48

Adj. R2 = 0.71 Number of Samples: 831

Table 7.6: Elasticities of GVO: Analysis of Results from Individual States


Punjab Andhra Pradesh

Constant (A) 8.826 (25.18) 4.794 (16.098

AC 0.005 (0.38) 0.015 (1.64)

FM 0.044 (2.49) -0.012 (-0.62)

IC 0.015 (1.87) -0.040 (-5.92)

A 0.742 (13.21) 0.176 (3.56)

L 0.250 (3.82) 1.067 (20.89)

DVL 0.126 (2.39) 0.159 (2.03)

DVCR 0.246 (5.6) 0.113 (1.44)

Adj. R2 0.86 0.64


Punjab : 300Andhra Pradesh : 531

76

Table 7.7: Labour Productivity Function Estimates: Pooled Data Analysis of Punjab and Andhra Pradesh



Constant (B) 6.061 45.95

AC/A -0.006 -0.8

FM/A 0.011 0.84

IC/A -0.023 -4.24

DVL 0.122 2.46

DVCR 0.240 5.25


Adj. R2 0.36


Table 7.8: Results on Labour Productivity Functions: State-Wise Results

VariableRegression Coefficients


Constant (B) 5.413 (20.14) 5.448 (46.98)

AC/A -0.053 (-2.95) 0.009 (1.13)

FM/A 0.090 (4.01) -0.027 (-1.73)

IC/A 0.014 (1.21) -0.032 (-5.25)

DVL 0.132 (1.91) 0.137 (2.08)

DVCR 0.0.309 (5.29) 0.113 (1.72)

Adj. R2 0.18 0.07

Notes: t – Values in Parenthesis : Number of Samples:


Table 7.9: Results on Capital Formation Function: Pooled Data Analysis of Punjab and Andhra Pradesh

77



Constant (C) 10.529 60.12

A 0.869 13.49

DVCR 0.132 1.06

DVL 0.037 0.27


Adj. R2 0.33


Table 7.10: Results on Capital Formation Function: State – Wise Results

VariableRegression Coefficients


Constant (C) 10.289 (67.72) 9.113 (42.96)

A 0.928 (14.68) 0.830 (8.70)

DVCR 0.230 (2.09) 0.037 (0.19)

DVL 0.193 (1.46) -0.052 (-0.26)

Adj. R2 0.43 0.12

Note: t – Values in Parenthesis : Number of Samples:


Table 7.11: Elasticities of GVO in Andhra Pradesh: Analysis of Results from Irrigated and Rainfed Farms


Irrigated Rainfed

Constant (A) 4.794 (16.98) 5.043 (3.68)

AC 0.015 (1.64) -0.005 (-0.18)

FM -0.012 (-0.62) 0.109 (1.58)

IC -0.040 (-5.92) NA

A 0.176 (3.56) 0.385 (2.73)

L 1.067 (20.89) 0.764 (3.08)

DVL 0.159 (2.03) 0.349 (1.35)

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DVCR 0.1013 (1.44) 0.423 (1.96)

Adj. R2 0.64 0.40


Irrigated : 531Rainfed : 69

Results for Farm-wise for all three sample states together and individual states

Table 7.12: Elasticities of GVO: Farm Group-Wise Results

VariablesMarginal andSmall Farms

Semi Medium andMedium Farms

Large Farms

Constant 3.20 (8.86) 5.39 (10.24) 7.24 (4.48)

AC -0.007 (-0.44) 0.007 (0.38) -0.126 (-0.98)

FM 0.067 (2.06) 0.065 (2.06) 0.160 (3.2)

A -0.112 (-0.9) -0.138 (-1.13) 0.558 (1.43)

L 1.409 (18.44) 1.068 (12.82) 0.594 (4.88)

DVL 0.223 (1.58) 0.082 (0.74) 0.245 (1.6)

DVIR -0.340 (-2.63) -0.421 (-3.27) 0.004 (0.02)

DVCR 0.038 (0.29) 0.234 (1.86) 0.058 (0.39)

DVY 0.342 (3.76) 0.169 (1.93) 0.064 (0.47)

DVS (AP) -0.829 (-5.39) -0.733 (-4.66) -0.778 (-3.67)

DVS (Orissa) -1.607 (-8.63) -1.773 (-8.66) -1.460 (-4.81)

Adj. R2 0.59 0.35 0.83


Marginal and Small : 552Semi-Medium and Medium : 746Large : 45

Table 7.13: Results on Labour Productivity: Farm Group-Wise Results


Semi Medium and Medium Farms

Large Farms

Constant (B) 4.57 (22.91) 5.81 (29.96) 6.85 (6.39)

AC/A 0.017 (1.7) 0.003 (0.27) -0.181 (-1.31)

FM/A 0.117 (5.5) 0.489 (2.73) 0.123 (2.26)

DVIR -0.320 (-3.5) -0.375 (-5.19) 0.0249 (0.11)

DVL 0.251 (2.64) 0.129 (2.08) 0.185 (1.1)

DVCR 0.089 (0.96) 0.234 (3.32) 0.036 (0.22)

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DVY 0.334 (5.15) 0.165 (3.38) -0.016 (-0.12)

DVS (Andhra Pradesh) -0.590 (-5.63) -0.752 (-8.58) -0.829 (-3.61)

DVS (Orissa) -1.337 (-10.28) -1.619 (-14.39) -1.774 (-5.62)

Adj. R2 0.37 0.48 0.71


Marginal and Small : 552Semi – Medium and Medium : 746Large : 45

Table 7.14: Results on Capital Formation: Farm Group – Wise Analysis


Semi – Medium andMedium Farms

Large Farms

Constant (C) 9.34 (30.07) 10.65 (45.39) 12.03 (6.22)

A 1.027 (5.14) 0.985 (8.38) 0.273 (0.36)

DVCR 0.549 (2.29) 0.124 (0.91) 0.420 (1.45)

DVL 1.09 (4.26) -0.119 (-0.99) 0.187 (0.63)

DVY 0.009 (0.05) -0.215 (-2.29) -0.567 (-2.39)


DVS (Orissa) -2.135 (-8.08) -2.452 (-15.96) -1.885 (-4.51)

Adj. R2 0.20 0.41 0.56


Marginal & Small Farms : 552Semi Medium and Medium Farms : 746Large Farms : 45

Farm Group-wise Results for Irrigated Farms in Punjab and Andhra Pradesh and Rainfed farms in Andhra Pradesh

Table 7.15: Elasticities of GVO: Farm Group-Wise Results

80



Large Farms

Constant 7.723 (25.25) 5.291 (14.62) 9.572 (6.11)

AC -0.002 (-0.25) 0.021 (1.73) -0.327 (-2.44)

FM -0.009 (-0.56) 0.027 (1.22) 0.183 (3.21)

IC -0.019 (-3.03) -0.038 (-4.47) -0.033 (-1.15)

A 0.577 (7.97) 0.070 (0.76) 0.495 (1.25)

L 0.623 (10.64) 1.070 (18.42) 0.601 (4.4)

DVL 0.227 (2.99) 0.169 (2.14) 0.283 (1.94)

DVCR 0.179 (3.07) 0.277 (3.55) 0.116 (0.73)

DVS (AP) -0.664 (-9.43) -0.778 (-7.68) -0.841 (-3.88)

Adj. R2 0.64 0.58 0.85



Table 7.16: Results on Labour Productivity: Farm Group-Wise Results


Semi Medium and Medium Farms

Large Farms

Constant (B) 6.174 (35.29) 5.928 (32.12) 8.430 (7.73)

AC/A -0.007 (-0.82) 0.008 (0.71) -0.364 (-2.48)

FM/A -0.031 (-1.71) 0.029 (1.62) 0.157 (2.51)

IC/A -0.018 (-2.63) -0.037 (-4.83) -0.054 (-1.46)

DVL 0.118 (1.51) 0.185 (2.94) 0.218 (1.38)

DVCR 0.184 (2.91) 0.278 (4.46) 0.124 (0.70)


Adj. R2 0.35 0.40 0.69


Marginal and Small : 319Semi – Medium and Medium : 476Large : 36

Table 7.17: Results on Capital Formation: Farm Group – Wise Analysis

81


Semi – Medium andMedium Farms

Large Farms

Constant (C) 10.135 (30.83) 10.310 (34.41) 9.969 (5.40)

A 0.915 (3.95) 1.192 (7.42) 0.983 (1.35)

DVCR 0.204 (0.89) 0.066 (0.43) 0.331 (1.21)

DVL 0.351 (1.17) -0.172 (-1.10) 0.242 (0.91)


Adj. R2 0.15 0.34 0.53


Marginal & Small Farms : 319Semi Medium and Medium Farms : 476Large Farms : 36

Table 7.18: Elasticities of GVO of Irrigated Farms in Andhra Pradesh: Farm Group-Wise Results



Large Farms

Constant 6.862 (19.03) 4.118 (9.80) 6.972 (2.54)

AC 0.005 (0.55) 0.024 (1.69) -0.265 (-1.48)

FM -0.036 (-1.80) 0.010 (0.36) 0.206 (2.61)

IC -0.029 (-4.04) -0.045 (-4.44) -0.023 (-0.35)

A 0.557 (6.32) -0.095 (-0.75) 0.483 (0.58)

L 0.688 (10.15) 1.206 (17.15) 0.714 (3.72)

DVL 0.279 (2.77) 0.133 (1.18) 0.511 (1.83)

DVCR 0.122 (1.52) 0.104 (0.84) -0.411 (-1.04)

Adj. R2 0.60 0.55 0.72



Table 7.19: Elasticities of GVO of Rainfed Farms in Andhra Pradesh: Farm Group-Wise Results



Large Farms

Constant 4.007 (1.64) 7.578 (4.36) -

82

AC 0.015 (0.37) -0.033 (-0.58) -

FM 0.127 (1.34) 0.031 (0.23) -

A 0.566 (1.21) 0.335 (0.80) -

L 0.964 (1.87) 0.425 (1.19) -

DVL 0.318 (0.62) 0.551 (1.77) -

DVCR 0.410 (0.76) 0.659 (2.24) -

Adj. R2 0.12 0.23 -



83

SECTION 8

CONCLUSIONS AND POLICIES FOR MAXIMIZING ON-FARM INVESTMENT

This study has examined the following.

1. Trends in savings and investments in Indian economy as a backdrop to analysis in

agricultural sector

2. The issues of financial inclusion, credit and indebtedness

3. A detailed analysis on farm profitability and price policy and implication for investment

in agriculture

4. Trends in public and private investment at macro level (CSO data) and on-farm private

investment using All India Debt and Investment Surveys.

5. Growth and compositional shifts in on farm capital assets using farm level data

6. Determinants of output, productivity and capital formation

7. Policy implications for maximizing on-farm investment in India

The conclusions and policy discussions are given below

8.1. Trends in savings and investments in Indian economy

The trends in gross domestic savings in India show that it increased from 8.6% in 1950-51 to

36.9% in 2007-08 before declining to 32 to 34% in 2008-09 and 2009-10 respectively.

Household sector plays an important role in savings as compared to private corporate

sector and public sector. As per cent of GDP, household sector savings increased from 5.7% in

1950-51 to 12.9% in 1980-81 to 18.4% in 1990-91 and to 24.1 % in 2003-04. It is important to

note that household savings constitute around two-thirds of gross domestic savings in the

country.

Household savings has two components viz., physical savings and financial savings. Both

components as per cent of GDP have increased over time. The share of financial savings in the

total household savings increased significantly from 25.0 per cent in the 1950s to around 47.0

84

percent in the five years ending 2006-07. Physical assets increased particularly since the early

2000s because of demand for construction particularly housing.

Regarding savings in the farm sector, it is known that majority of population in India depend on

agriculture sector. Therefore, savings in this sector are important for investment. Inspite of its

importance, we do not have estimation of savings in farm sector. There are constraints of

estimation of farm sector savings. Based on a survey on assessment situation of farmers, GOI

(2009) tried to estimate savings for farm economy. According to their estimates, consumption is

higher than income and there are dis-savings of about -2.8% of GDP in 2003. The gap is filled by

borrowing by the farm households.

The overall gross capital formation as proportion of GDP in the year 2002-03 was 2.1%. This

analysis shows that there is a wide gap between investment and savings in the farm sector. It also

indicates that mobilisation of savings is important for raising investment in farm sector.

Gross capital formation in India increased from 11.2 per cent of GDP in the 1950s to 36 to 38 per

cent in the late 2000s. Household investment as per cent of GDP rose from 4.7% in the 1950s to

12.7% in 2003-07. Private corporate investment increased particularly in the 2000s significantly.

Savings-investment gaps reveal interesting trends. This gap for household sector rose

significantly from 1.9% in the 1950s to 11.1% in 2003-07. In other words, savings are much

higher than investment for household sector. In the case of private corporate sector and public

sector, there are negative savings as investments are higher than savings. In other words,

household sector savings are supplying funds for other sectors for investment. The composition

shows that the share of household sector in total gross capital formation showed fluctuations

from 20% in 1982-83 to 50% in 2002-03. During 2005-06 to 2008-09, the share declined and it

was around 30 to 34%.

The analysis in this study shows that household sector plays an important role in savings

and investment of the economy. However, in the case of farm economy, cultivators, on

average, are dis-saving. There is a need for increase in household savings in farm economy

in order to increase investments.

8.2. Financial inclusion, credit and indebtedness

85

This study highlights the importance of financial inclusion in improving the living conditions of

poor farmers, rural non-farm enterprises and other vulnerable groups and discusses few issues

and challenges. The concept of financial inclusion covers wider financial services including

credit, savings, insurance etc. We have noted that financial exclusion in terms of access to credit

from formal institutions is high for small and marginal farmers and some social groups. For

example, even in a state like Andhra Pradesh, 73% to 83% of outstanding loan for small and

marginal farmers is from informal sources like money lenders and traders. Supply and demand

problems have to be solved with appropriate policies. Banks should look at financial inclusion

both as business opportunity and social responsibility. Apart from formal banking institutions,

the role of self help group movement and MFIs is important to improve financial inclusion of

people. However, some regulatory procedures for MFIs may have to be evolved by having

consultations with MFIs, consumers and Government. Depoliticization of the financial system is

needed for maintaining the viability of formal financial institutions. Risk element of small and

marginal farmers and other vulnerable groups have to be taken into account in framing policies

for financial inclusion. For improving the productivity of small and marginal farmers and

improving the skills of rural non-farm workers, the banking system may have to undertake credit

plus advisory services.

Ultimately, the financial inclusion is successful only if the productivity of the small and marginal

farmers, rural non-farm enterprises and other and vulnerable groups is sustained with viable

economic activities. We have to recognize that financial inclusion for farmers can not be

sustained by the banking system alone as there is a need for other measures like public

investment in irrigation, research and extension, infrastructure in rural areas, proper seeds and

fertilizers, good marketing system for better price etc. Small and marginal farmers face many

risks in cultivation. Financial inclusion should take into account the risk element of farmers

while framing policies. Banks should do credit plus services to the farmers and rural non-farm

sector. The agricultural officers must provide ‘farm advisory’ services that will help in making

agriculture an integrated activity with appropriate backward and forward linkages (Rangarajan,

2005). Rural banking has to be restructured so that credit will be supplemented with farm and

non-farm advisory services.

8.3. Farm Profitability, Price Policy and Implications for Investment in Agriculture

86

The agricultural price policy has been largely successful in playing a major role in regard to

providing reasonable level of margins of around 20% over total costs to the farmers of both rice

and wheat. In turn, it seems to have encouraged farmers’ investments in yield increasing

technology and in increasing production and enabling sufficient procurement to act as buffer

stock and physical access to food by achieving and maintaining self-sufficiency. The needs for

supplies to the PDS and various poverty alleviation programmes have also been increasing at a

faster rate. The price policy could help in procuring 43 mt in TE 2008-09 compared to a mere 13

mt in TE 1982, providing buffer stocks for an offtake of 38 mt in TE 2008-09, which is a steep

increase over just 14 mt in TE 1982. These huge tasks of production, procurement and

distribution would not have been possible without the efficient working of the country’s price

policy. The country is by and large insulated from supply shocks because of its operation. For

example, the prices of cereals increased by only 20% while they spiked by 150% in the

international market during 2005-08. This does not mean that there are shortcomings in its

working, but only to highlight the fact its utility far outweighs any such problems to be rectified.

Nevertheless, the agricultural price policy does face some new challenges in the recent period

with reduced non-price interventions in the form of investments in agriculture and also

percolation of some of the global price volatility through open trade. In fact, the analysis in this

study shows that these two are mainly responsible for higher support prices. The trend of

declining cost of production with higher growth in yields got reversed in the nineties and beyond

and they went up at nearly 1.5% per annum for rice and wheat. The returns over paid-out costs

also for rice farmers declined at 1.15% per annum in real terms leading to distress for them. This

declining profitability seems to have discouraged them in increasing spending on yield

augmenting technology as shown by the relatively declining growth rate of cost of

cultivation.

The price intervention in enhancing MSPs for wheat in 1997-98, 2006-07 and 2007-08, keeping

in view of the fact that the market prices are higher, has distorted the intercrop price parity

between rice and wheat. Though the costs of production are similar for these two crops since the

mid-nineties, the wheat MSP has been 14% higher than that of paddy since then and up to 2007-

08. In the recent period, the rice farmers have also suffered from lower price realization than the

respective MSPs since 2000-01, lower (7%) returns over total costs compared to 27% in wheat

87

and higher growth in costs of production compared to the whole sale price indices between 2002-

03 and 2006-07. On the whole, the analysis presented in the study shows that there is some merit

in the argument that the MSP of rice should be closer or slightly below that of wheat. The recent

hikes in support prices for rice are justified in this background.

The averages tend to mask regional variations and the impacts of price policy in a vast country

like ours with divergent climatic conditions. The cost of production is higher than all-India

average in some of the poorer states due to low productivity and do not cover all costs. But, the

price realisation does cover variable costs and leave a reasonable margin over that in all the

states. At the same time, the prices realized cover all costs in states producing efficiently at low

cost.

The analysis in the study indicates that higher emphasis has to be given for non-price

interventions through public investments and private investments to supplement price policy

measures17. They can help in increasing yields, reduce the exclusive reliance on prices for farm

profitability and food security, and also hasten poverty reduction, as the history of poverty

reduction in the country shows that the proportion of the poor declined at faster rates when the

food prices are low18. Decentralising the procurement operations by building necessary

infrastructure in states like UP, Bihar, MP, Orissa is critical in achieving equity in this regard.

Also, price support operations need to be extended to other crops like pulses and oilseeds to

stimulate their production. The storage capacities at present for buffer stocks are sufficient to

store less than 30 million tones, while the actual needs often go beyond 50 mt. Therefore,

measures to increase the storage capacities have to be initiated immediately and at the same time

the quality of the stored grain needs to be given equal importance by upgrading the technology.

The option of involvement of private players in procurement and storage may also be explored

subject to retaining the public control in view of food security needs.

To sum up, farmers’ profitability and remunerative prices are important for higher

investments in agriculture. It may be noted, non-price factors like investment in

17 Several scholars have argued for yield increasing growth path for agricultural development to reduce adverse impact on the poor (Dantwala 1986; Krishnaji 1990; Rao 1994).18 See for a detailed exposition Dev and Ranade (1998) and Dev and Ravi (2007)

88

infrastructure, technology, credit, water etc. are important for arresting decline in

profitability of farming. This can increase on-farm investment.

8.4. Trends in public and private investment in agriculture at macro level and on-farm private investment

The share of private investment in total investment in agriculture increased significantly over

time from about 50% in the early 1980s to 80% in the decade of 2000s. It may be noted that

90% of the private investment is made by farmers for on-farm production. Therefore, there

is a need for increasing on –farm investment.

The growth rates of investment show that public sector investment showed a negative growth in

the 1980s and 1990s and a growth of 15 per cent in 2000s. On the other hand, growth rate of

private investment increased gradually from 2.5% in the 1980s to 4.1% in the 1990s and 5.2% in

2000s. On the whole, the growth rate of public and private investment is the highest in the

decade of 2000s.

One indicator for investment is the share of gross capital formation (GCF) in agriculture as a

proportion of agricultural GDP. This ratio has been stagnant at around 14 per cent during 2004-

05 to 2006-07. However, there has been a significant increase in the 11 th five year plan period.

The ratio increased to 16.3% in 2007-08 and further to 19.67% in 2008-09 and to 20.30% in

2009-10. However, the share of GCF in agriculture in overall GDP has remained stagnant at

around 2.5 to 3.0%. Due to this, the share of GCF in agriculture and allied sector in total GCF

has remained in the range of 6.5 to 8.2% during 2004-05 to 2009-10. Therefore, there is a need

for increase in investments in agriculture in India.

We examined trends in on-farm investments using the data from All India Debt and Investment

Surveys for the years 1991-92 and 2001-02. The trends in composition show that there was a

significant increase in the share of farm houses, wells and irrigation while the shares of land

improvement and transport equipment declined. There was also some improvement in the share

of agricultural machinery. It indicates that the growth of capital on farm houses, wells and other

irrigation and agriculture machinery is much higher than land improvements and transport

89

equipment. If we concentrate on composition in 2003, farmers invest the highest amount in wells

and other irrigation followed by agricultural machinery, transport equipment and land

improvements. It is a matter of debate whether farmers should invest more in well irrigation too

much when the ground water is depleting in many areas. They should invest in areas where

ground water is plenty. There are significant regional variations in the composition across states

in India. The share of non-irrigation machinery increased for majority of states.

Analysis on capital intensity, land and labour productivities and rural poverty

This study examines the inter-relationships of capital intensity, land and labour productivities

and rural poverty across states of India. We have divided the fifteen states into three categories

of top 5 states, middle 5 states and bottom 5 states. Table 5.6 divides states into three categories

viz., top 5 states, middle 5 states, bottom 5 states19. In the top 5 states, three states viz., Punjab,

Kerala, Haryana are common in capital, labour productivity and rural poverty. Tamil Nadu, West

Bengal, Gujarat and Rajasthan are common in at least two categories. States are also common in

most of the cases in middle five states. In the bottom five states, Madhya Pradesh, Orissa and

Bihar are least capital intensive with least agricultural productivity and high rural poverty.

Maharastra is common in land and labour productivities and rural poverty. The analysis shows

that capital intensity increases land and labour productivities which in turn reduces rural poverty.

It shows the importance of farm investment for reducing poverty.

Term Credit

Another way of looking at farm investment is to look at term credit. We examined the sub-sector

wise ground level term credit flow for agriculture & allied activities for 2001-02 and 2009-10.

Term credit is mostly used for investment purposes. In 2001-02, ‘others’ category has a share of

45% and the details are not known. Next farm mechanization has the highest share with 18%

followed by hi-tech agriculture (10.5%) and animal husbandry(10.3%). Minor irrigation has a

share of 8.6% in 2001-02. In the year 2009-10, the share of hi-tech agriculture increased

significantly to 43.5% from 10.5% in 2001-02. The shares of land development and plantation

and horticulture also increased. It looks like farmers are spending more on horticulture now. The

share of minor irrigation and farm mechanization declined.

19 We have not included here J&K and Himachal Pradesh

90

8.5. Growth and compositional shifts in farm capital assets using farm level data

At all India level, land capital growth was more than 3 per cent per annum while non-land

growth of capital assets was only 0.72 per cent per annum. Among non-land capital, the growth

rates of animal capital (-0.74%) is found to be negative, while the growth rate of irrigation

capital (1.16) and non-irrigation machinery (1.93) are found to be positive.

At the state level, eight out of 15 states showed positive growth of non-land capital assets while

13 out of 15 states recorded positive growth if we include land in the capital assets. It shows that

land values have increased significantly over time in many states. In the non-land assets, highest

growth rate was recorded by Tamil Nadu (4.55%), followed by Maharashtra (4.41%), Rajasthan

(2.35%), Himachal Pradesh (2.28%), Haryana (1.68%), Bihar (1.49%) and Punjab (1.3%). Six

states viz. Andhra Pradesh (-3.33%), Gujarat (-1.71%), Karnataka (-2.73%), Kerala (-

6.45), Madhya Pradesh (-0.05%), Uttar Pradesh (-1.24), West Bengal (-1.93%) showed

negative growth in non-land capital assets. It is a concern for these states regarding decline

in on-farm capital in agriculture. In irrigation machinery, six states recorded positive growth

while 11 out of 15 states showed positive growth for non-irrigation machinery.

Regarding levels of shares in 2007-08, the shares of animal capital are more than 35% in Andhra

Pradesh (0.39%), Himachal Pradesh (38%), Karnataka (47%), Orissa (55%) and West Bengal

(46%). The share of irrigation machinery was more than 40% in Gujarat (41%), Maharashtra

(46%), Rajasthan (56%) and Tamil Nadu (64%) in 2007-08. Similarly, the share of non-irrigation

machinery was more than 40% in seven states viz., Bihar (68%), Haryana (42%), Himachal

Pradesh (49%), Madhya Pradesh (42%), Orissa (45%), Punjab (46%), and Uttar Pradesh (63%).

8.6. Determinants of output, productivity and capital formation

Our analysis on the determinants of gross value of output, labour productivity and capital

formation reveal the following.

In aggregative terms (cutting across the results from individual States and farm groups); animal

capital, farm machinery, land, labour and credit availment have turned out to be important

positive determinants of farm level GVO; animal capital, farm machinery, literacy and credit

91

availment are found to have positive impact on labour productivity; land, credit availment and

literacy level of farmers are identified as major positive determinants of capital formation at farm

level.

Regarding irrigated and rainfed data sets, Animal capital, land, labour, literacy and credit

availment are the major positive determinants of GVO of irrigated farms, and in case of rainfed

farms farm machinery, land, labour, literacy and credit availment have positive impact on GVO.

Farm group wise analysis of irrigated farms suggests that animal capital, land, labour, literacy

and credit availment have positive impact on GVO of marginal and small farms, animal capital,

labour, literacy and credit availment in case of semi – medium and medium irrigated farms, and

farm machinery, land, labour and literacy in case of large farms. With respect to rainfed marginal

and small farms, and semi – medium and medium farms, farm machinery, land, labour, literacy

and credit availment are found to have positive impact on GVO.

8.7. Policies for maximizing on-farm investment in India

Government has to have policies to induce on farm investment like irrigation capital, farm

machinery, animal capital, land value.

Our analysis above has shown that animal capital, farm machinery, land, labour, credit and

literacy are determinants of output and productivity. Land, credit and literacy are identified as

major positive determinants of capital formation at farm level.

The fixed investments by the farmers are generally made in well Irrigation, other irrigation

sources, agricultural implements, machines and transport equipments, reclamation of land, farm

houses, orchards and plantations, bunding and other improvements. One of the major factors

determining private investment is public expenditure including investment. Private investments

can be expected to grow given the complementary effects of public investments mentioned

earlier. Further, investments in public works programmes (e.g. Mahatma Gandhi National Rural

Employment Guarantee Act, MGNREGA) that create infrastructure, generate employment and

incomes and poverty alleviation programmes that improve the asset base of the poor would also

92

act as a catalyst in increasing private investments in agriculture.20 The Government's subsidies

are also responsible for private investments like tubewell irrigation.

Regarding other factors, a study by Gandhi (1996) showed that rural savings and cooperative

credit, followed by the extent of use of high-yielding varieties (HYVs), level of agricultural

wages and commercial bank credit positively influenced private capital formation. The

institutional credit seems to be one of the crucial variables in determining private investment.

Terms of trade in favour of agriculture is another important variable influencing private

investment. One of the important policies relate to governance e.g. property rights and law and

order. Second one is facilitating development of rural financial institutions to have savings,

insurance and credit. We examine these factors below.

Public Investment

Public investment in rural physical infrastructure like roads, electricity, marketing, irrigation and

social infrastructure like education and agriculture research influences on-farm investment. In

recent years, the importance of development public expenditure in reducing poverty has been

recognized. Fan et al (1999) examine the causes of decline in rural poverty in India and

particularly, the study concentrates on the impact of government spending on productivity and

poverty. The study finds that investment in rural roads and agricultural research and development

have the greatest impact, while government spending specifically targeted to poverty reduction

such as employment programmes have only modest effects.

The question of subsidies in agriculture has emerged as an important issue in recent policy

debates. Undoubtedly, subsidies are effective in pushing agricultural growth to a certain extent,

but it is important to make sure that they do not become a permanent feature of the Indian

economy.

Input subsidies are having adverse effect on environment in agriculture. These policies are

leading to degradation of land and water. These subsidies caused severe deterioration of the

systems due to the neglect of their maintenance in addition to becoming fiscally unsustainable.

20 See Mahendra Dev (1996) on the direct and indirect benefits of public works programmes

93

Further, they have led to the highly wasteful use of canal water, ecological degradation from

water logging, salinity, pollution, excessive consumption of electricity, and over drawl of ground

water resulting in the shortage of drinking water in several parts of the country. Similarly, the

prevailing heavy subsidy on nitrogenous fertilizers perpetuates inefficiencies in the domestic

fertilizer industry. Irrigation and use of power seems to be as high under small farm as compared

to large farms. However, these are cornered by the farmers in irrigated areas and those in

unirrigated areas do not get these subsidies. Most of the fertilizer subsidy also goes to the

farmers under irrigated area. The benefit flowing to the farmers and consumers of food is

illusory, as it is leading to the degradation of soil on account of excessive chemicalisation and

adverse NPK ratio. A fixed quantity of fertilizers sufficient for one or two hectares may be

subsidized for all the farmers, if necessary through a system of input coupons, requiring them to

purchase the remaining quantities in the market at the going rates.

Who gets these subsidies? During the initial stages of the adoption of new technology in

agriculture some of these subsidies may be justified as 'front-up costs’. Over time it was found,

that the richer states and well-irrigated areas, certain crops, and sometimes rich farmers captured

a disproportionately high share of the major input subsidy programmes of fertilizer, power,

irrigation and credit.

Another issue regarding subsidies is that whether these should be withdrawn without improving

the efficiency in supplying inputs. While withdrawing subsidies, care should be taken to remove

inefficiencies in production and distribution of inputs and services For example, a farmer may

not pay the full cost of power if reliable and continuous electricity is not supplied. The

distribution system is characterized by inefficient transmission and widespread pilferage.

Irrigation system is characetrised by inflated costs on account of bad design, inferior quality of

services and inefficiencies in management, delays and leakages in construction. Due to these

inefficiencies, the actual subsidy going to the farmers using these inputs is far less than what is

projected. A case for reducing subsidies will be strengthened if the input use efficiency

improves.

94

There has been a secular decline in public investment and it has been a concern as it is important

for improving infrastructure till 10th five year plan. In the 11th five year, there has been increase

in public investment significantly. However, this is not enough and the investment in agricultural

research is still less than 1% of GDP.

There seems to be some trade-off between input subsidies and public investment in agriculture.

The problem of mounting subsidies and its effect in terms of crowding out public agricultural

investment has been highlighted in the 10th Plan document21. The estimates of CSO's public

sector investment comprise mainly of investment in irrigation projects. Some researchers feel

that this is an underestimate and there is a need for widening the definition of public investment

by including investment in infrastructure, like rural roads and electrification. Government

allocates large funds to anti-poverty programmes. Some of these programmes also may be

contributing to capital formation in agriculture.

The four central government’s special programmes viz., National Food Security Mission

(NFSM), Rastriya Krishi Vikas Yojana (RKVY), National Horticulture Mission (NHM) and

Agricultural Technology Management Agency (ATMA) would be useful, if implemented

properly, in improving private investment in agriculture.

Rural infrastructure and Bharat Nirman: Investment in rural infrastructure is more important

for agricultural growth than trade liberalization per se. The role of public and private investment

in infrastructure becomes crucial in this context. The rural infrastructure plays an important role

in both input and output sides. It helps to ensure timely and adequate delivery of inputs to the

farmers and on the output front integrating local markets with national and international markets.

In this context, the announcement of Bharat Nirman programme in 2005 by the Government of

India in order to improve agriculture and rural infrastructure is in the right direction. It covers six

components of infrastructure development: accelerated irrigation benefit programme, accelerated

rural water supply project, construction of rural roads, rural housing, providing rural

electrification and telephone connectivity in the villages. However, the progress has been slow in

this programme.

21 More on subsidies vs. investments, see Gulati and Narain (2003)

95

Terms of Trade

Economic reforms in India have improved terms of trade for agriculture and opened up new

opportunities such as benefits from trade and specialization, widening choices in new technology

including bio-technology, increase in private investment in irrigation and marketing

infrastructure like storage and transport. It is viewed that protection to industry in the form of

import substitution policies like tight import controls and high import duties have hurt the

agriculture till 1991. Disprotection to industry since 1991 are supposed to correct this bias and

increase terms of trade for agriculture. “This would create a potentially more profitable

agriculture, which would be able to bear the economic costs of technological modernization and

expansion” (Manmohan Singh, 1995, p.2)22.

A look at terms of trade (TOT) in post-reform period shows that it was favourable to agriculture

with fluctuations. Agricultural growth was 3.7% per annum in the first six years of the reform

period (1991-97). The terms of trade in agriculture improved during this period due to dis-

protection to industry, devaluation of rupee and increase in minimum support prices. Then the

growth rate started declining since mid-1990s. The TOT deteriorated during this period.

Agricultural growth has picked up again and growth was more than 4% during the period 2004-

08. There seems to be a revival in TOT again during this period. Thus, the favourable TOT in

agriculture has some impact on agriculture in the post-reform period. Similarly, private

investment in agriculture improved in the post-reform period although there has been stagnancy

in recent years. Terms of trade for agriculture based on GDP implicit price deflators indicate the

TOT increased significantly since 2004-05. Particularly, the TOT for agriculture increased

significantly in 2007-08 and 2008-09 and they are the highest in the last two decades.

However, price policy and terms of trade alone can not improve agricultural productivity unless

we improve non-price factors like irrigation, credit, technology etc.

Table 8.1. Agriculture Terms of Trade based GDP Implicit Price Deflators (1999-00 =100)

22 Also see Ahluwalia (1996)

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Year Term of Trade for Agriculture1989-90 88.91990-91 89.81991-92 96.41992-93 93.31993-94 93.91994-95 95.81995-96 96.21996-97 97.61997-98 101.11998-99 101.01999-00 100.02000-01 97.12001-02 96.32002-03 97.32003-04 95.72004-05 93.42005-06 96.82006-07 97.72007-08 101.42008-09 103.4

Note: GDP implicit price deflators for agriculture and non-agriculture are used to derive agricultural terms of tradeSource: Estimated based on National Accounts Statistics, CSO.

Fig 8.1. Terms of trade for agriculture based on GDP implicit price deflators

Term of Trade

85.00

87.00

89.00

91.00

93.00

95.00

97.00

99.00

101.00

103.00

105.00

198

9-90

199

0-91

1991

-92

1992

-93

1993

-94

1994

-95

199

5-9

6

1996

-97

1997

-98

199

8-9

9

1999

-00

2000

-01

2001

-02

2002

-03

2003

-04

200

4-0

5

2005

-06

2006

-07

2007

-08

2008

-09

Year

Term of Trade

Source: Estimated from National Accounts Statistics, CSO.

It may also be noted that higher relative food prices hurt the poor. It may be mentioned here that

there are many of the poor who are unambiguously hurt by the risingrelative price of food. Higher

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food prices hurt all households who are net purchasers of food. Apart from the whole of the urban

population who are net purchasers, even among the rural households, more than 50% of the

households are net purchasers of food. Despite the fact that National Sample Survey tabulations do

not reveal which rural households are net consumers or producers of food, there is sufficient proxy

evidence. More than 50% of households are agricultural households with some land or landless

households. Also we have marginal farmers with very small holdings.

Thus by a very conservative estimate at least 50% of the total rural population is adversely

affected by an increase in the relative price of food. This is an ex ante distributional effect,

independent of the effect on mean per capita consumption (the “mean effect”) of higher food

prices, i.e. if nominal income distribution was held constant. Note that some of the small

producers who have a marketable surplus could become worse off with higher prices. This is

because typically a small producer sells the surplus immediately in the post harvest season, when

prices are low, and buys food during the lean season (when she depletes her own stock) when

prices are high.

Financial inclusion and credit

We already discussed above on policy issues relating to financial inclusion and credit. According

to the expert group on Financial Inclusion (GOI, 2008), only 27% of farmers have access to

institutional credit. It is true that there have been some improvements in flow of farm credit in

recent years. However, the Government has to be sensitive to the four distributional aspects of

agricultural credit. These are: (a) not much improvement in the share of small and marginal

farmers23; (b) decline in credit-deposit (CD) ratios of rural and semi-urban branches; (c) increase

in the share of indirect credit in total agricultural credit and; (d) significant regional inequalities

in credit.

Institutions

23See Rangarajan, 2005

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It is not only public investment but also institutions for efficient implementation and delivery

systems are needed for promoting private investment.

Institutional reforms are important, particularly in the domain of public systems, for sustained

technical progress and output growth in agriculture. “There is a limited scope for privatizing

irrigation, research and extension and other infrastructure facilities. All of these will continue to

be mainly the responsibility of public sector. Unless the public sector’s efficiency in mobilizing

resources and managing these facilities is vastly improved, trade and price policy reform will not

make a significant difference to the pace of agricultural growth” (Vaidyanathan, 1996, emphasis

added).

Institutions for Inputs and Output Marketing

The increasing costs of purchased inputs, as well as the problems of quality in terms of sub-

standard and spurious seeds and pesticides have also figured as the dominant proximate factors

for the crop failures, given the drought conditions. This has also been recognised as a crucial risk

factor linked to the distress of farmers. Therefore, appropriate institutions have to be developed

for delivery of inputs, credit and extension especially for small farmers. We already discussed

about the importance of marketing. There are different models for marketing collectively by the

small and marginal farmers. One is self help group model. For example, women in Andhra

Pradesh are procuring maize from farmers. Second model is co-operative model similar to sugar

and dairy co-operatives. Third one is small producer co-operatives or links with corporates like

the contract farming. Some experiences with direct marketing have been quite successful such as

Uzhavvar Santhail in Tamil Nadu, Rythu Bazars in Andhra Pradesh and Apni Mandi in Punjab

and Rajasthan.

Institutions for sustainable land and water management

Environmental concerns are among the policy priorities in India. Particularly degradation of land

and water is alarming. Watershed development under the new guidelines, in general, has an

overall positive impact on environment. However, groundwater tables are depleting at an

alarming rate. The de facto privatization of groundwater and subsidized power supply are the

99

main culprits. There has been a neglect of minor irrigation sources like tanks. Shortage of

drinking water has accentuated and quality of water has declined over time.

An integrated approach is needed for water resources management in the country. An appropriate

strategy should integrate institutional approaches with market principles. Since institutional

innovation (Water user associations) is already in place for canal irrigation, it is time now to

implement volumetric pricing. There is a need to de-link water rights from land rights in order to

ensure equity and sustainability.

Institutions like the water user associations (WUAs) and watershed committees are important for

water management. The experience of Andhra Pradesh shows that the impact of WUAs has been

encouraging in these areas, especially in terms of providing irrigation to tail end farmers. This

has been made possible by cleaning of canals and water courses and monitoring of water losses

by the WUAs. Area under paddy is reported to have increased significantly following reforms.

However, much of the reported increase could be statistical because of underreporting of

irrigated area before reform , as this meant lesser payment of water tax to revenue department.

Paddy yields are reported to have increased by 40%.

Irrigation charges were increased by more than three times from 1997 in Andhra Pradesh. Even

so, the surface water rates will at best cover maintenance charges, whereas in the case of lift

irrigation the farmer also bears the full capital cost of the well or bore. Another notable

development was that the works were executed by WUAs themselves at lesser cost instead of

getting them done by contractors. But the vested interests lost no time in adjusting to the new

situation by presidents of the WUAs acting as contractors. This and other malpractices invited

the wrath of farmers who in several cases used the provision in the Act for recall of the

presidents. The only long-term solution to this is awareness building and promoting participatory

monitoring and evaluation. Unlike in the case of canal irrigation, WUAs are not found to be

effective in respect of tank irrigation due to insufficient allocations.

In the case of land and forestry, watershed approach and Joint Forest Management are crucial for

protecting the environment. The critical issue is sustainability of these programmes. Although

100

watersheds have shown positive economic impact, the social issues are missing. More

participatory approach and involvement of women would lead to sustainability of watershed

development approach. In the case of JFM, the focus is more on high income areas like timber.

Low value products constituting sources of livelihoods for the poor have low priority. Customary

rights of the tribals on podu (shifting cultivation) have to be recognised.

Awareness and involvement of the civil society is a precondition for checking environmental

degradation. Environmental movements would have a discerning impact in this regard.

Another concern is the land degradation due to excessive use of fertilizers and pesticides.

Government has programmes such as Integrated Pest Management (IPM) and Integrated Nutrient

Management (INM). Keeping in view the ill effects of pesticides and also National Policy on

Agriculture, Integrated Pest Management Approach (IPM) approach has been adopted as a

cardinal principle and main plank of plant protection in the country in the overall crop

production programme. Besides ongoing activities, the thrust area will be pertaining to Pest Risk

Analysis (PRA) and post entry quarantine surveillance. This has become essential in the light of

WTO agreement, which will facilitate more and speedier movement of plants, planting materials

globally.

Integrated Nutrient Management (INM) advocates the integrated use of all sources of plant

nutrients like chemical fertilizer, bio-fertilizer and locally organic manures like farm yard

manure, compost, vermi-compost, green manures, edible and non-edible oil cakes to maintain

soil health and its productivity. Focusing on improving soil quality should be one of the priority

areas in raising agricultural growth. Organic farming is also being encouraged in the country due

to demand for these products all over the world.

Property Rights

101

Lack of property rights particularly on land and law and order issues are also hindering on-farm

investment in agriculture.

Land relations are extremely complicated and this complexity has contributed significantly to the

problems facing actual cultivators. Unregistered cultivators, tenants, and tribal cultivators all face

difficulties in accessing institutional credit and other facilities available to farmers with land

titles. One priority is to record and register actual cultivators including tenants and women

cultivators, and provide passbooks to them, to ensure that they gain access to institutional credit

and other inputs. In such registration, the onus should not be on the tenant to prove his/her

tenancy, but on the landlord to disprove it. As part of the reforms, lease market should be freed

and some sort of security for tenants has to be guaranteed. This will ensure availability of land

for cultivation on marginal and small farmers. The land rights of tribals in the agency areas must

be protected. There is considerable scope for further land redistribution, particularly when waste

and cultivable lands are taken into account. Complementary inputs for cultivation (initial land

development, input minikits, credit, etc.) should be provided to all assignees, and the future

assignments of land should be in the name of women.

On land market, the Report of the Steering Committee recommended the following. “Small

farmers should be assisted to buy land through the provision of institutional credit, on a long

term basis, at a low rate of interest and by reducing stamp duty. At the same time, they should be

enabled to enlarge their operational holdings by liberalizing the land lease market. The two

major elements of such a reform are: security of tenure for tenants during the period of contract;

and the right of the land owner to resume land after the period of contract is over” (GOI, 2007).

Basically, we have to ensure land leasing, create conditions including credit, whereby the poor

can access land from those who wish to leave agriculture. There are some emerging land issues

such as increase in demand for land for non-agricultural purposes including special economic

zones, displacement of farmers, tribals and others due to development projects. There is a need

for careful land acquisition. Land alienation is a serious problem in tribal areas.

The share of women in Indian agriculture is increasing. Inspite of their importance, women are

continued to deny property rights and access to other productive resources. Providing women’s

rights in land, enhancing infrastructure support to women farmers, and giving legal support on

102

existing laws, will get recognition for women as farmers and enable them to access credit, inputs,

and marketing outlets. Names of women should be recorded as cultivators in revenue records, on

family farms, where women operate the land having ownership in the name of male members.

There needs to be a comprehensive directive across the country that in all government land

transfers, women’s claims are directly recognized, be they transfers for poverty alleviation,

income generation (crop cultivation, fish cultivation), or resettlement. The profound gender bias

in the functioning of institutions for information, extension, credit, inputs and marketing needs

urgent correction, taking into account their mobility, domestic responsibilities and social

constraints.

103

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