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The purpose of this paper is to understand the factors determining the incidence and extent of indebtedness among agricultural households in rural India.

This study is based on a nationally representative survey carried out under the 70th Round of the National Sampling Survey Office (NSSO) across rural India. Data on household characteristics, farming characteristics, indebtedness and extent of outstanding credit have been extracted from the comprehensive survey data. Four research hypotheses have been formulated and tested using simple statistical techniques. Further, using the Heckman Selection Model, the study assesses the factors determining the agrarian indebtedness among households in rural India.

The results from the descriptive analysis show that there is a significant difference in socio-economic and farm characteristics of indebted and non-indebted households. Further, the level of indebtedness differs across sources of the loan, landholding sizes and geographical locations among agricultural households. The results of regression analysis clearly indicate that household characteristics, farm characteristics and sources of loan determine both the incidence and extent of indebtedness among agricultural households.

The main limitation of the study is that only the data giving information regarding the amount of outstanding loans have been collected, and there is no information regarding the amount of credit availed, the purpose and the due date of payment. Further, there is scope to improve the robustness of the empirical model by adding and modifying explanatory variables.

There are only a limited number of empirical studies providing an understanding of the factors determining the indebtedness of agricultural households in rural India. Hence, this study is a good value addition to the existing literature.

According to the 70th round of the National Sample Survey published by the Government of India in 2014, the incidence of indebtedness among households in the rural areas of Telangana state, India, is twice that of rural all-India. Around 59 per cent of rural households are indebted in Telangana as against 31 per cent all-India. The purpose of this paper is to examine the extent and magnitude of indebtedness among rural households in the Medak district of Telangana state. Further, the authors wanted to identify the sources of credit to these households and for what purpose the loans were utilised.

To achieve the objective, the authors conducted a primary-level household survey in one of the distressed districts in newly formed state. The authors applied the Bayesian and the Lasso regression methods to identify the factors that impact indebtedness of a household.

The OLS results based on the Lasso regression results show that among all the explanatory variables, principal occupation, use of modern technology, the rate of interest, household medical expenditure and source of loan are significant, indicating that these variables significantly affect the loan taken by the farmers in the study area. The study shows that alternative sources of non-farm income and promotion of modern technology in agriculture can reduce the incidence of farmers’ indebtedness in India.

The paper contains significant information with regard to indebtedness. It focusses on the issue troubling the authorities the most. It provides the ground realities of the incidence of indebtedness in Medak, one of the most distressed districts of Telangana, a Southern Indian state. There have been very few similar studies done in the newly formed state. The paper has employed an advanced statistical technique, i.e. Heckman’s selection regression technique, to study farmers’ indebtedness in India. It provides a means of correcting for non-randomly selected samples, which otherwise can lead to erroneous conclusions and poor policy.

By examining the role of farming attitudes and motivations, the aim of this paper is to provide a framework for better understanding farmers' behaviour in relation to the decision to obtain credit.

Using a nationally representative survey of farm operators in Ireland, this paper derives explanatory variables (based on a factor analysis of respondents mean ratings of 13 attitudinal statements) representing three different farming motivations. An ordered logit model is then formulated to examine the effect of farming attitudes as well as personal characteristics and farm structural variables on the degree of indebtedness.

Personal characteristics of the farmer such as age and education as well as farm structural variables such as farm size and farm system were all found to strongly affect decisions in relation to credit use. The study identified how farmers are not just driven by business related goals such as maximising profits but are also strongly motivated by productivist tendencies and perceived lifestyle benefits associated with farm work. These underlying farming motivations were, in turn, found to have a differential impact on credit use. Specifically, business orientated attitudes were found to provide a prime incentive for farmers to borrow funds. On the other hand, farmers who strongly value the benefits associated with the farming lifestyle were less likely to look for credit.

Past research has focused on the effect of socio‐demographic characteristics and farm structural variables in examining differences in farm indebtedness. This study extends this literature by specifically examining the role of farming attitudes. Obtaining a deeper understanding of the factors that affect the level of farming debt will be important as the degree of indebtedness has been found to affect farmers' management decisions. Outside of explaining farm credit use, farming attitudes and motivations may have an important impact on farmers' behaviour in relation to a variety of farm activities.

The purpose of this paper is to provide empirical evidence on the relationship between capital structure and technical efficiency (TE) for Italian cereal farms during the 2008–2014 period. Emphasis is given in the understanding of the relationship between the level of financial leverage for cereal farms and their production performance.

The methods employed in this research article are based on non-parametric techniques in order to derive TE estimates for a sample of Italian cereal farms based on available Farm Accountancy Data Network data to explore in depth the relationship amongst the financial exposure of the sector and the capacity to utilise an efficient and effective production technology. Furthermore, subsidies are considered in the model as a non-discretionary variable and therefore, as an input that farmers cannot directly influence within the production function. Hence, the non-discretionary Data Envelopment Analysis model is a more appropriate framework since it is not penalising farms at a lower level of Pillar I payments when benchmarked with farms that receive a higher level of payments.

The results show that significant improvements could be achieved for most of the farms in the sample by improving production and management practices. Furthermore, results provide an empirical support of the adjustment theory by showing a negative impact of debt to asset ratio to TE.

This research article provides a first insight on the evolution of the Italian cereal farms debt-TE relationship in periods where high price instability has been observed.

Purpose – The globally controversial genetically modified (GM) cotton has been adopted widely by Indian farmers. Claiming the adoption to be a success of the GM technology, the GM proponents call for a large-scale introduction of GM crops in Indian agriculture. Opposition to GM crops is largely constructed in terms of the environmental risks that GM technologies pose to crop and forest biodiversity. This chapter examines the economic and political context in which these seeds were adopted to see if adequate support mechanisms were available to farmers to facilitate adoption of the new technology.

Design/methodology/approach – A field study was conducted in Vidharbha, Eastern Maharashtra. In addition, government reports and newspaper articles were reviewed and interviews were conducted in Maharashtra and Delhi.

Findings – This chapetr finds that the problems faced by farmers are much deeper than what technology can solve or which have been addressed in the GM debate. Cotton farmers face persistent problems in the agricultural production process that increase their production costs. A spate of farmer's suicides in Vidharbha and other rain-fed regions of India epitomizes the dire conditions farmers are in. This chapter asserts that state-supported policies transformed India from a food importing to a food surplus country in the 1960s during the green revolution. However, GM cotton has been introduced without a supportive infrastructure for technology transfer in Maharashtra and most cotton-growing states. The lack of support makes the gain of cotton farmers in Vidharbha from the new technology highly uncertain.

Originality/value – This analysis shows the need to examine the role of government programs in helping farmers implement technological advances in agriculture.

This study analyzes the personal and farm characteristics that influence the use of farm credit, the degree of indebtedness, and debt consolidation for U.S. farms. Whereas previous studies have examined the supply side of agricultural credit using lender‐based data, this study considers the demand side of agricultural credit using representative farm‐level data from the USDA’s 2001 Agricultural Resource Management Study (ARMS). The results show that gross farm income, risk management strategies, and operator’s age and risk aversion had significant influences on the likelihood of farm credit use by rural residence, intermediate, and commercial farms.

The purpose of this paper is to investigate the role of crop insurance among Hungarian crop farmers and the responses to the introduction of the two-scheme risk management system. Specifically, first, it examines the economic and environmental factors affecting the willingness of farmers to contract crop insurance. Second, it reveals the relationship between having crop insurance and technical efficiency of crop producing farms.

Probit models of panel data are applied to explore the factors of insurance decisions. The relationship between efficiency and insurance is investigated with two-stage data envelopment analysis (DEA) model with double bootstrap using panel data for the 2001 to 2014 period.

The results of Probit model estimations show that the education, the size, the indebtedness of crop producing farms and the new two-scheme risk management system are in positive correlation, while the concentration of farming activity are in negative correlation with the crop insurance contracting. The estimations of two-stage DEA model reveal that crop producing farms with an agricultural insurance contract are more efficient than the farmers without using this risk management tool.

Empirical investigation of the influencing factors of agricultural insurance demand in Hungary and the examination of the relationship between insurance and technical efficiency may contribute to the development of Hungarian risk management system.

During the 1930s Franklin Roosevelt’s New Deal created a wide range of spending and loan programs. Brief descriptions are provided for the programs created by the New Deal and loan and spending programs that were in place before the New Deal. I worked with others to create a panel data set with estimates of the spending and lending by the programs each year from 1930 through 1940. The data aggregated to broad categories are reported here and the methods and sources used to construct the estimates of the spending and lending for the categories are discussed.

The purpose of this paper is to examine how debt affects the cost structure of a farm. Agency costs arise when stakeholders of a farm manage their farm differently to obtain debt which results in inefficiencies. These inefficiencies cause a farm to deviate from cost minimization strategies.

This study uses the non-parametric technique of data envelopment analysis. Through this method, a non-stochastic cost frontier is constructed where all farms must lie on or above the frontier. This allows for the analysis of how debt affects the shape of the cost frontier and for how debt affects deviations away from cost-minimizing strategy. The shadow costs of the debt constraints in the linear programming problem are used to analyze the effect of debt at the cost frontier while a series of Tobit models are estimated to examine the effect of debt on deviations away from the frontier.

The findings of this paper support the existence of agency costs associated with debt for Kansas farms. The addition of debt and capital constraints lowered the minimum cost frontier increasing the average efficient cost under variable returns to scale. However, for those farms on the frontier, the shadow cost of debt was negative meaning an increase in debt would lower the overall variable cost. The increase of debt was found to be negatively correlated to the efficiency score of the farms.

This paper provides value by supporting the existence of agency costs which has been disagreed upon in the literature and also providing new insights for how to analyze agency costs. Since debt was found to have a negative shadow value for those farms on the frontier but negatively correlated with efficiency scores, this suggests that agency costs affect firms differently depending on where the farm is on the cost frontier.