Arindam Laha and Pravat Kumar Kuri*

Transcription

1 ARTICLES Rural Credit Market and the Extent of Tenancy: Micro Evidence from Rural West Bengal Arindam Laha and Pravat Kumar Kuri* I INTRODUCTION Ind. Jn. of Agri. Econ. Vol.66, No.1, Jan.-March 2011 In many developing countries, it has been found that the rural credit market is imperfect in nature. There are substantial variations in the availability of formal credit in rural-urban locations. In the process of agrarian transformation, the availability of credit plays an important role in influencing land-leasing decisions. In fact, it is widely believed that the land rental market acts as an institution to adjust the differences in the access to rural credit (Jaynes, 1982; Eswan and Kotwal, 1989). The higher the extent of credit, greater is the extent of lease-in-land and vice versa. However, the effect of credit constraints on land leasing behaviour could be different under alternative tenurial arrangements (Narayan, 2001). An asset poor farmer can overcome a credit constraint by interlinking sharecropping contract with the provision of credit. In contrast, under the rental arrangement, the extent of lease in land is bound to decline with the credit constraint. Empirical literature exploring the impact of credit constraint on the leasing behaviour of the farmer households is relatively scanty. In a significant contribution to the existing literature, Kocher (1997) 1 found that formal credit does not play so much an important role on land leasing decisions as the ownership of land does. The aggregative analysis as carried out by Kocher was criticised by Narayan (2001) on the ground that it fails to capture the differential role of credit in land leasing decisions under alternative tenurial contracts and suggested an alternative specification which can provide a rationale for the insignificant result of the role of credit in explaining leasing decisions. In fact, Narayan (2001) examined the role of credit on the nature of land leasing decision under alternative tenurial contracts in a disaggregated manner. It was postulated in the study that large farmers are more likely to enter into rental contracts and would therefore be less likely to lease in land if they are credit constrained while the marginal or small farmers would lease more land under sharecropping contracts if they were credit constrained. Empirically the proposition was verified and it was found that credit has a positive impact on land leasing decision for small farmers and a negative impact for large farmers. One of the *Lecturer, Department of Commerce and Reader, Department of Economics, respectively, University of Burdwan, Burdwan (West Bengal).

2 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY 77 limitations of the study was that the author separated out the effect of contractual arrangements by using farm size as a proxy variable and accordingly the impact of credit on land leasing was tested. Lack of data on appropriate variables that can explain the contractual choice restricted the study to test the hypothesis separately for the sharecroppers and renters. This paper attempts to fill in this gap of research problem through a detailed analysis of micro-level data generated by a primary survey from rural West Bengal. 2 The broad objective of this paper is to examine the impact of credit on land leasing decisions using micro evidences from rural West Bengal. The specific objectives of the study are: (1) to examine how the extent of tenancy of farming households is guided by the availability of credit. To address the issue, a conceptual framework has been developed to analyse the behaviour of tenants in the presence of credit constraints, (2) to identify the underlying factors influencing the land leasing decisions and to evaluate the role of credit in determining such decisions in a disaggregated manner under alternative forms of tenancy. For convenience, the paper is divided into four sections. In the next section, a theoretical model has been outlined by extending the model of Bliss and Stern (1982) and Kuri (2003) to consider the role of credit on the extent of tenancy. The propositions of the theoretical model have been tested empirically using primary survey in rural West Bengal both under Partial and Perfect Adjustment Models and presented in Section III. The conclusions and policy implications are outlined in Section IV. II EXTENT OF TENANCY: A THEORETICAL MODEL The conceptual framework of the model is based on the behaviour of a farmer who maximised profit subject to a credit constraint. In this optimisation problem, it is assumed that the credit market is imperfect in nature, i.e, the availability of credit is assumed to depend on the exogenously given land owned by the farmer, L(V) = φv + ϕ( φ 0, ϕ 0) where L is the available credit amount which depends on owned land holding, V. Here ф determines the extent to which the access of credit is dependent on land ownership. If φ is positive, then it can be found that even landless agents have access to some credit (Eswaran and Kotwal, 1986). It is also assumed that the farmer held some liquidity ( L ) to self-finance the working capital or to meet the living expenditure (w) requirement. The production function is written as Q = f(le, x, A,V) where Q is output, l is the amount of labour input, e is the average level of effort applied to these units of labour (i.e., le is the labour in efficiency units), x is the amount of current inputs, A is the desired cultivated area, V is the owned land holding.

3 78 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS In the land lease market, a contract is defined by three parameters (α, β, r) where α is the output share of the tenant, β is the cost share of the tenant and r is the fixed rent per unit of land in terms of money. Depending on the values of (α, β, r), different types of contract 3 can be specified as: Fixed rent contract: α = 1, β = 1 and rф0. Owner contract: α = 0 and β = 0. Pure sharecropping contract: α (0,1) and β=0. Cost sharing contract: and α (0,1) β (0,1). Now a farmer s optimization problem is given by Max α cf (le, x, A, V) [r (A V) + β px] (1+i) s.t.r(a V) + βpx + w L + L Where c is the price of output, p is the price of input, i is the rate of interest on loan. In the optimisation problem, the farmer makes their decision on desired land holding A, subject to the credit constraint. It is assumed that the tenant borrow credit to meet up the rental share of the landlord and to share the cost of current inputs. We define Lagrangian function as l = αcf(le, x, A,V) [r(a V) + βpx](1+ i) + λ[l + L r(a V) βpx w] where λ is the shadow price of credit constraint. The Kuhn-Tucker conditions for optimisation are l f x f x = αc + αc (r + βp )(1+ i + λ) 0 A x A A A l A 0 A = λ = L + L r(a V) βpx w 0 λ λ λ = 0 λ A 0, λ 0 When the credit constraint is not binding (i.e., λ =0), the loan amount does not affect the desired land holding. However, when the credit constraint is binding (i.e., λ 0 ), the optimum value of the desired land holding is determined by the credit constraint. Differentiating the constraint yields, the following comparative static result:

4 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY 79 A 1 = 0 L x r + βp A The above condition states that the desired land holding increases with the increase in loan size. Thus from the above model, it can be postulated that the amount of credit determines the optimum value of the desired land holding. In order to capture the idea, we have extended the framework of Bliss and Stern (1982) and Kuri (2003). In the modified version, a new variable, amount of credit, is incorporated in the desired land holding function as A = G(le, NT, L) where NT is the non-tradable assets held by the farmer. It is hypothesised that an increase in the efficiency units of labour, non-tradable assets and the amount of loan availability resulted in an increase in the desired cultivated area. Mathematically, G G G G 1 = > 0,G 2 = > 0andG 3 = > 0 le NT L Partial Adjustment Model: Net land rented in (NLRI= Total area leased in-total area leased out) is assumed to be a linear function of the difference between the desired cultivated area and the owned cultivable area, i.e., NLRI = λ(a V) where λ is the adjustment factor, which is assumed to be a positive constant. The larger the difference between A and V, the higher will be the extent of tenancy and vice versa. If we assume that the full adjustment in owned land and desired cultivated areas is not possible because of the credit constraint from the supply side and the other cost of cultivation in the tenancy market, then λ<1. Thus under the partial adjustment model, λ lies between zero and unity, i.e, 0<λ<1. Substituting desired land function in NLRI, we get, NLRI = λ[g(le, NT,L) V] Taking linear approximation of the above function, we get, NLRI = C + λg 1 le + λg 2 NT + λg 3 L λv where C is a constant. Thus the equation to be estimated is: NLRI = a1 + a 2le + a 3NT + a 4L a 5V + U where a 1 =C=a constant, a 2 =λg 1, a 3 =λg2, a 4 =λg 3 and a 5 =λ, U is the random disturbance term satisfies all the classical least square assumptions. In a situation of asset based lending policy pursued by the formal credit market, the amount of loan

5 80 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS and owned land holding are positively related. Hence, a multicollinearity problem may arise in the case of the partial adjustment model. Perfect Adjustment Model: The assumption of perfect adjustment between the desired cultivated area and owned cultivable area imply that the adjustment factor becomes unity, λ=1. Then NLRI = A V CULT = NLRI + V = A = G(le, NT, L) where actual cultivable area implies the sum of owned land and net land rented in taking linear approximation, we get CULT = C + G 1 le + G 2 NT + G 3 L The equation to be estimated is / / / / / CULT = a1 + a 2le + a 3NT + a 4L + U / / / / / where a 1 =C=a constant, a 2 = G 1, a 3 = G 2, a 4 = G 3 and U is the random disturbance term satisfying all the Classical Least Square assumptions. Availability of Credit and Extent of Tenancy: A Diagrammatic Presentation The role of credit in determining cultivable area can be explained with the help of Figure 1. The original framework of the diagram is given by Sadoulet et al. (2001). We have incorporated the role of access to credit on the behaviour of land market transactions. In the diagram 45 degree line implies cultivated farm size equals owned land. Any deviations from the line imply renting land in or out. Thus the curve Figure 1. Impact of Credit Availability on the Cultivated Land of the Household

6 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY 81 ABCDEFG implies cultivated area curve. Along this curve, there comprises a class of pure landlords (segment OA), cultivating landlords (segment AB), owner operators (segment CD), owner tenants (segment DE) and cultivating landlords (segment FG). Let us consider the impact of credit availability on the decision of the extent of cultivable area of the tenant. It is assumed, as before, that in an imperfectly developed credit market, the access to credit is proportionately related to own land holding of the tenant. Credit availability generates the necessary liquidity to start production and to enter the rental market as a tenant at a lower level A1 of land ownership. With an access to the larger amount of the loan, the cultivated farm size is also larger. Hence, cultivated area is greater for large households at every level of land ownership. III REGRESSION RESULTS AND DISCUSSION: PARTIAL AND PERFECT ADJUSTMENT MODEL In the study, we examine the impact of credit on land leasing decision by disaggregating the analysis separately for tenants under three alternative tenurial arrangements, i.e. fixed renter, pure sharecropper and cost sharecropper. In the empirical analysis, the impact of credit on the land leasing decision is measured by running an Ordinary Least Square Regression with a dependent variable, net land rented in (NLLI) in Partial Adjustment Model and cultivated land (CULT) in Perfect Adjustment Model. Variables relating to household specific characteristics (HHAGE, HHEXP, HHEDU, AGL, NFIRM), asset of the tenant (BULLOCK, OWNL, VEQIP), land-specific (PERIRI, FRAG) and loan specific characteristics (FL, LALAND, CLASSI) are included in our analysis to determine whether these variables are significant determinants of land leasing decision under alternative mode of tenurial cultivation or not. Loan specific variables are of our special interests as any positive coefficients of these variables would suggest that the impact of credit on the extent of tenancy varies according to the type of contract choice chosen by the farmer household. The descriptions of explanatory variables and hypothesis (as specified by expected sign) in both the partial and perfect adjustment models are presented in Table 1 and 2 below: Model specification (1) TABLE 1. LIST OF DEPENDENT VARIABLES Dependent Variable (2) Description (3) Partial Adjustment Model Net Land Rented In (NLLI) Land holding (in bigha) Perfect Adjustment Model Cultivated land (CULT) Land holding (in bigha)

7 82 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS TABLE 2. EXPECTED SIGN OF THE DETERMINANTS TO DETERMINE THE EXTENT OF TENANCY Independent variables (1) Notation (2) Description (3) Age of the head of the household HHAGE Age in years + Experience of the head of the household Expected sign (4) HHEXP Squared of age in years 5 + Education of the head of the household HHEDU Years of schooling - Family members employed in non-firm activity Family members employed in non-firm activity AGL NFIRM Family members working in farm activity (in units) 6 + Family members working in non-firm (in unit) Owned land 4 OWNL Owned land (in bigha) + Proportion of irrigated land to total land holding PERIRI Proportion of irrigated land (in percentage) Fragmented holding FRAG Number of fragmented holding - Value of animal BULLOCK Total number of pair of animals held + by the household (in Rs.) Value of equipment VEQIP Total value of agricultural equipments held by the household (in Rs.) + Formal loan FL Amount of formal loan (in Rs.) Loan from landlord LALAND Amount of informal loan from landlord (in Rs.) + Classification of household based on the availability of credit CLASSI Dummy variable, 1 if bank-customer and 0, otherwise + In conformity with the theoretical conceptualisation, the empirical analysis considers the impact of credit on land leasing decisions of the households. Land leasing decisions of the household is captured by the variable net land rented in (NLLI) in partial adjustment model, and cultivated area (CULT) in perfect adjustment model. In order to have a deeper insight, the study disaggregated the analysis separately for tenants under three alternative tenurial arrangements, i.e., fixed renter, pure sharecropper and cost sharecropper. The possibility of multiple contract choice by a particular household is also taken into account in the disaggregated analysis. A comprehensive data set of 149 holdings, observed on 106 tenant households in the study, has been used to estimate the partial and perfect adjustment models. The results of Ordinary Least Squares regressions in both the models are presented in the Tables 3 and 4.

8 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY 83 TABLE 3. DETERMINANTS OF THE EXTENT OF TENANCY UNDER ALTERNATIVE TENURIAL ARRANGEMENTS IN PARTIAL ADJUSTMENT MODEL Dependent variable: Net land rented in holding (in bigha) Fixed rent Pure sharecropping Cost sharecropping Variable (1) Coefficient (2) T-statistic (3) Coefficient (4) T-statistic (5) Coefficient (6) T-statistic (7) CONSTANT HHAGE * HHEXP ** HHEDU * * AGL NFIRM *** ** OWNL *** ** PERIRI FRAG *** VEQIP 2.02E *** 2.34E *** * BULLOCK * FL *** 2.78E LALAND * CLASSI ** Observations R-squared Source: Field survey Note: The statistical analysis has been made using E-Views statistical package., and Significant at 10, 5 and 1 per cent level, respectively. TABLE 4. DETERMINANTS OF THE EXTENT OF TENANCY UNDER ALTERNATIVE TENURIAL ARRANGEMENTS IN PERFECT ADJUSTMENT MODEL Dependent variable: Cultivated land holding (in bigha) Fixed rent Pure sharecropping Cost sharecropping Variable (1) Coefficient (2) T-statistic (3) Coefficient (4) T-statistic (5) Coefficient (6) T-statistic (7) CONSTANT HHAGE HHEXP HHEDU *** AGL ** NFIRM *** PERIRI FRAG * VEQIP 2.21E E ** * BULLOCK ** FL *** LALAND E ** CLASSI ** Observations R-squared Source: Field Survey Note: Same as in Table 3.

9 84 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS In these regressions our particular interest is the variables relating to credit. We find that formal credit (FL) has a positive impact on the land leasing decision of the fixed renters, cost sharecroppers and pure sharecroppers in both versions of the model (Tables 3 and 4). But the result is statistically significant only in the case of fixed renters. Another useful way to look at this result is to look at the impact of the borrower s classification (CLASSI) on the land leasing decision. Here also we find that a bank customer can easily adjust his operated area with the provision of subsidised credit from institutional source. Again the result is found significant only in the case of fixed rent tenancy. On the other hand, the empirical results suggest that the loan from landlord (LALAND) has a positive impact on land leasing decision of the cost sharecroppers and pure sharecroppers in both versions of the model. But the result is statistically significant only in the case of cost sharecroppers. The intuition of the result is that the extent of tenancy under the alternative contracts is affected by the availability of credit from institutional sources. Greater the availability of formal credit, the more profitable it is for the tenant to cultivate more land under fixed rent tenancy. On the other hand, the non-availability of formal credit compels a farmer to depend on informal sources mostly on the landlord. Relaxing the working capital constraint by the landlord can enable the tenant to lease in more cultivated area under the cost sharecropping contract where the landlord shares at least a part of the production cost with the tenant. However, neither formal credit nor loan from the landlord is found to be statistically significant in determining land leasing decisions of pure sharecroppers in both versions of the model. The regression result gets support in the following Table 5 where sources of credit are classified on the basis of alternative tenurial contracts. The sources of credit are divided into three broad sources: institutional, landlord and informal lenders other than the landlord. In reality, households have many sources of availability of credit. For simplicity of analysis, we assume that all sources of credit are mutually exclusive in the sense that coexistence of many sources of credit is ruled out. If a particular tenant is getting credit from institutional sources, then all other sources of credit are not taken into consideration. Again a tenant, with no access from the institutional sources of credit but getting credit from both landlord and other informal sources, is recorded to have an access of credit from the landlord only. An informal source other than the landlord is recorded as the source of credit of a tenant only if neither the institutional source of credit nor loan from the landlord is a viable alternative to him. In the table we find nearly 49 per cent of the fixed rent tenants are getting institutional credit. Thus being a bank customer, a fixed rent tenant can use the rental market to equilibrate the ratios of non-marketable or imperfectly marketable inputs to ownership land holdings. It also found that cost sharecroppers are heavily dependent on landlord for necessary production and consumption credit. Nearly 56 per cent of them were borrowed from the landlord in the planting season. Thus it can be argued that the smaller the tenant s wealth, the more profitable it is for the landlord to specify a cost sharing contract.

10 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY 85 TABLE 5. CLASSIFICATION OF THE VARIOUS SOURCES OF CREDIT UNDER ALTERNATIVE TENURIAL CONTRACTS Sources of credit Informal sources other Types of contract (1) Institutional (2) Landlord (3) than landlord (4) Total (5) Fixed rent 18 (48.64) 10 (27.03) 9 (24.33) 37 (100.00) Pure sharecropping 9 (37.50) 5 (20.83) 10 (41.67) 24 (100.00) Cost sharecropping 2 (22.22) 5 (55.56) 2 (22.22) 9 (100.00) Both fixed rent and 17 (47.22) 16 (44.45) 3 (8.33) 36 (100.00) sharecropping Total 46 (43.40) 36 (33.96) 24 (22.64) 106 (100.00) Source: Field survey However, no general conclusion can be drawn in the case of pure sharecroppers. In the dataset, we have found neither the landlord nor any formal credit institution is the major source of credit for pure sharecropper. In fact, various informal sources other than the landlord have found one of the important sources of credit for pure sharecroppers. It is followed by institutional credit which financially included 37.5 per cent of pure sharecroppers into its network. Thus an explanation is given in support of our earlier empirical observation that: neither formal credit nor loan from the landlord is found to be statistically significant in determining land leasing decisions of the pure sharecroppers in both versions of the model. The above analysis suggests that credit has a significant impact on the farmer s decision to lease in land in both partial and perfect adjustment models. The results of both these models are found similar in argument. Further it can be pointed out that formal credit has a positive effect on the land leasing decision of fixed rent tenancy, while loans from the landlord has the same effect in the case of cost sharecroppers. However, one of the limitations of the analysis is that in this paper, attention is restricted only to the decision to lease in land, neglecting the determinants of the decision to lease out land. Representation of a small number of landlords in our sample observations restricted the authors to examine the determinants of lease out land. 7 With a better dataset, one can further test the proposed hypothesis that credit can also have an impact on the leasing out market under different contracts by doing separate regressions on land lease in and lease out markets. IV CONCLUSION AND POLICY IMPLICATIONS The mechanism of land rental market is mostly guided by the availability of rural credit. The types of tenancy contracts to be followed by the farmers are greatly influenced by the nature of the availability of credit. The credit linkages to land lease market has been captured in a theoretical framework. The stylised features of both the Perfect and Partial Adjustment Models have been tested empirically by using micro evidences of rural West Bengal. More specifically, the impact of credit on land

11 86 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS leasing decision is tested by disaggregating the analysis separately for three alternative tenurial arrangements, like fixed rent, pure sharecropping without cost sharing and cost sharecropping tenancy. The empirical findings establish the argument that the farmers with access to institutional credit prefer to opt for more lease in land under fixed rent contact vis-à-vis pure sharecropping contact. On the other hand, the availability of informal credit from the landlords encourages them to opt for cost sharing form of sharecropping. The findings of the study have brought about some insightful policy implications to create an enabling environment for increasing agricultural productivity by modifying and facilitating appropriate rural institutions, namely, rural credit and tenurial contracts. The choice of tenurial contract has a far reaching implication on the allocation of resources and, thereby, on the desired agricultural productivity. If sharecroppers are free to choose the amount of labour used in production, usually, they allocate less labour which in turn, resulted in lower intensity of cultivation and lower output per acre compared to owner cultivation and fixed rent mode of contract. Sharecropping is thus subjected to sub-optimal allocation of resources and hence considered to be an inefficient mode of cultivation vis-a vis fixed rent tenancy (Marshall, 1890, Bardhan and Srinivasan, 1971, Bell, 1977, Laha, 2009). Moreover, it is also an empirically established fact that sharecropping contract along with interlinked informal credit contract is grossly exploitative in nature (Bhaduri, 1983). One of the major findings of our study is that the availability of credit acts as a facilitator in the choice of tenurial contract and fixed rent tenancy is found to occur at a greater frequency with institutional credit. It thus warrants effective institutional reforms especially with respect to rural credit so as to influence the choice of efficient mode of tenurial contract. This is expected to create an enabling environment for tackling distortions in the outcome of higher productivity in agriculture. In other words, the provision of institutional credit and thereby encouragement of a fixed rent form of tenancy is expected to reduce the extent of exploitation under the informal interlinked tenancy contract which in turn, can bring about an efficiency in the allocation of resources and higher agricultural productivity. Received February Revision accepted December NOTES 1. Kocher (1997) examined whether the production decisions of Indian farm households in Uttar Pradesh are affected by the lack of access to formal credit through examining the impact of formal credit on household transaction in the land rental market. 2. Taking into consideration the co-existence of the incidence of tenancy and the extent of rural credit transactions, the study purposively selected a particular block, namely Raina I, from the district of Burdwan in West Bengal. The block, Raina I, comprised two distinct agro climatic zones-one, with canal irrigation and the other zone with rainfed farms. A two stage sampling design was followed in order to select the households of the survey. In the first stage, the villages, namely, Saktia and Anguna from Mugura village panchayat were chosen as developed villages, whereas the villages, like, Dhamash and Boro were chosen from Natu village panchayat as underdeveloped villages under the same criteria. In the second stage, 203 households were purposively chosen covering the different categories of farmer households.

12 RURAL CREDIT MARKET AND THE EXTENT OF TENANCY Tenancy contracts are of three types: fixed rent tenancy, pure sharecropping without cost sharing and cost sharing arrangement under sharecropping. Under fixed rent tenancy, the landlord leases out the land to the tenant and in return asks for a fixed rental payment. The sharecropping is a form of tenurial contact under which tenant leases in land from the landlord and shares the output under predetermined contractual arrangement. The sharecropping is again of two types. The cost sharing sharecropping under which the landlord shares the cost of factor of production usually in the same proportion as the share of output. On the other hand, under the arrangements of the sharecropping without cost sharing tenants bear the full cost of production and the proportion of output share to the landlord is usually smaller than under cost-sharing arrangement. Inclusion of cost sharing arrangement in our analysis is particularly relevant in view of the widespread prevalence of the arrangement as a part of the tenancy contract, which is a striking new phenomenon in Indian agriculture (Bardhan, 1984). 4. The variable, OWNL is only used in the Partial Adjustment Model to determine the extent of tenancy by the cultivator households. 5. In the literature on development economics, experience of the household is approximated by the squared of the head of the household. 6. In order to avoid the problem of multiplicity of employment, we recorded the allocation of time spent in the particular economic activity. 7. In the empirical survey, it was not possible to collect information on those absentee landlords who reside outside the village for most part of the year as they were usually involved in non agricultural activities as primary occupation. It was even difficult to collect information on landlord households residing within the village. Bardhan and Rudra (1980) excluded the category of lessors households from their sample on consideration of reliability of information. REFERENCES Bardhan, P. (1984), Land, Labour and Rural Poverty: Essays in Development Economics, Oxford University Press, Delhi. Bardhan, P. and T.N. Srinivasan (1971), Crop Sharing Tenancy in Agriculture: A Theoretical and Empirical Analysis, American Economic Review, Vol. 61, pp Bardhan, P. and A. Rudra (1980), Terms and Conditions of Sharecropping Contracts: An Analysis of Village Level Survey Data in India, Journal of Development Studies, Vol. 16, No.3. Bell, C. (1977), Alternative Theories of Sharecropping: Some Tests Using Evidence from Northeast India, Journal of Development Studies, Vol. 13, No.4. Bhaduri, A. (1983), Economic Structure of Backward Agriculture, Academic Press, London. Bliss, C.J. and N.H. Stern (1982), Palanpur: The Economy of an Indian Village, Clarendon Press, Oxford. Eswaran, M. and A. Kotwal (1986), Access to Capital and Agrarian Production Organisation, The Economic Journal, June, pp Eswaran, M. and A. Kotwal (1989), Credit and Agrarian Class Structure, in Bardhan (Ed.) (1989) Economic Theory of Agrarian Institution, Oxford University Press, New Delhi. Jaynes, G.D. (1982), Production and Distribution in Agrarian Economics, Oxford Economic Papers, Vol. 34, pp Kocher, A. (1997), Does Lack of Access to Formal Credit Constraint Agricultural Production? Evidence from the Land Tenancy Market in Rural India, American Journal of Agricultural Economics, Vol. 79, pp Kuri, P.K (2003), Factor Market Imperfections and Explanations of Tenancy: Testing of a Econometric Model Using Evidence from Assam of North-East India, Indian Journal of Agricultural Economics, Vol. 58, No.2, April-June, pp Laha, Arindam (2009), Land Tenancy and Rural Credit Market: A Case Study in West Bengal, Ph.D. Thesis, The University of Burdwan. Marshall, A. (1890, 1920, 1964), Principles of Economics, Macmillan, London, U.K. Narayan, T.A. (2001), Does Credit Constrain Land-Leasing Decisions? Fresh Evidence from India, Preliminary draft, Department of Agricultural and Resource Economics, University of Maryland, July 15. Sadoulet, E., R. Murgal and A. D. Janvry (2001), Access to Land via Land Rental Markets, in Janvry et al. (Eds.) (2001), Access to Land, Rural Poverty and Public Action, Oxford University Press, pp