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The purpose of this paper is to examine the performance of the agricultural banking industry using both traditional and risk‐adjusted non‐parametric efficiency measurement techniques. In addition to computing efficiency scores, the risk preference structure of the agricultural banking industry is examined.

The paper used data envelopment analysis (DEA) to examine the efficiency of agricultural banks in the year 2001. Standard cost efficiency is computed and compared to both profit and risk‐adjusted profit efficiency scores. The risk‐adjustment is a modification of traditional DEA wherein firm preferences are represented via a mean‐variance criterion. The risk‐adjusted technique also provides estimates of firm level risk aversion.

Results from the traditional approach that does not account for risk indicate a low degree of efficiency in the banking industry, while the risk‐adjusted approach indicates banks are much more efficient. On average, 77 percent of the inefficiency identified by the standard DEA formulation is actually attributable to risk averse behavior by the firm. In addition, most banks appear to be substantially risk averse.

The risk‐adjusted DEA technique used in this study should be applied to other, diverse data sets to examine its performance in a broader context.

Results from this study support the idea that traditional DEA methods may mischaracterize the level of efficiency in the data if agents are risk averse. In addition, the paper outlines a practical method for deriving firm level risk aversion coefficients.

This paper sheds light on the agricultural banking industry and illustrates the power of a new efficiency and risk analysis technique.

The Farm Service Agency (FSA) direct farm loan program provides credit to family‐sized farms including those operated by beginning farmers and socially disadvantaged applicants. Approximately 37% of all U.S. farms are estimated to be eligible for FSA direct loans when farm size, credit needs, farming experience, and occupation are taken into account. However, market penetration rates for various borrower cohorts range from 0.8% to 4.6% for FY 2000S2003. In general, beginning farmers have weaker financial characteristics than non‐beginning farmers. Yet, the same result is not found when comparing socially disadvantaged farmers with non‐socially disadvantaged farmers, such that there are few significant differences or the differences in financial characteristics are mixed. Overall, results indicate FSA direct farm loan borrowers have weaker financial characteristics than eligible, non‐FSA direct farm loan borrowers, implying FSA is serving farmers likely to be denied credit by commercial lenders.

The concept of student behaviour provides a tool for describing and understanding the underlying mechanisms between academic success as a dependent variable and individual determinants of students and the institutional context of study as independent variables. Defined as the micro-level characteristics that encompass students' actual behaviour and transitions within higher education, student behaviour influences the outcomes of academic performance, learning outcomes, the duration of studies, completion rates and future career paths. Student behaviour therefore serves as an intermediary construct between inputs and student outcomes. This chapter provides a comprehensive heuristic framework of student behaviour, drawing on insights from a range of disciplinary theoretical perspectives, including education, psychology, sociology, economics and political science. The conceptual model outlines the central role of student behaviour within the student life cycle and its implications for higher education research. In doing so, the chapter offers a conceptual panorama that encompasses both the factors that explain student behaviour and the phenomena that student behaviour itself influences, including its relationship to the concept of student engagement. The framework is not limited to conceptual delineation but invites further theoretical development.