Daniel M. Settlage, Paul V. Preckel and Latisha A. Settlage
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
This paper sheds light on the agricultural banking industry and illustrates the power of a new efficiency and risk analysis technique.
Details
Keywords
Yuan feng Zhao, Zhihui Chai, Michael S Delgado and Paul V Preckel
The purpose of this paper is to assess the effect of crop insurance on farmer income in Inner Mongolia, China
Abstract
Purpose
The purpose of this paper is to assess the effect of crop insurance on farmer income in Inner Mongolia, China
Design/methodology/approach
We use a survey of farmers in Inner Mongolia, China, with difference-in-difference, propensity score matching, and hybrid propensity score matching difference-in-difference treatment effect estimators to assess the effectiveness of crop insurance on farmer income.
Findings
The empirical results show that crop insurance does not significantly affect farmer income under the current policy of “low-premium, wide-coverage, low-guarantee and low-indemnity.”
Research limitations/implications
A possible limitation of this study is that the data includes only one geographic area, Inner Mongolia, China, and so results may not generalize to other regions of China.
Practical implications
This research provides empirical estimates of the impact of crop insurance on farm household income. Given the results, we speculate that a number of specific changes to the crop insurance program might increase its positive impacts.
Originality/value
We believe this is the first study to use individual farm household level survey data to evaluate the impact of crop insurance on farmer income in China.
Douglas Miller, James Eales and Paul Preckel
We propose a quasi–maximum likelihood estimator for the location parameters of a linear regression model with bounded and symmetrically distributed errors. The error outcomes are…
Abstract
We propose a quasi–maximum likelihood estimator for the location parameters of a linear regression model with bounded and symmetrically distributed errors. The error outcomes are restated as the convex combination of the bounds, and we use the method of maximum entropy to derive the quasi–log likelihood function. Under the stated model assumptions, we show that the proposed estimator is unbiased, consistent, and asymptotically normal. We then conduct a series of Monte Carlo exercises designed to illustrate the sampling properties of the quasi–maximum likelihood estimator relative to the least squares estimator. Although the least squares estimator has smaller quadratic risk under normal and skewed error processes, the proposed QML estimator dominates least squares for the bounded and symmetric error distribution considered in this paper.
To explain how cumulative efforts contribute to learning and literacy development.
Abstract
Purpose
To explain how cumulative efforts contribute to learning and literacy development.
Design/methodology/approach
A representation of how efforts lead to lasting growth is discussed through a variety of historical and current perspectives across content disciplines. This chapter includes depictions of how positive experiences can promote further success and recognizing one’s cumulative efforts and the effects from those are fundamental to educational attainment.
Findings
The value one places on tasks such as reading or writing is often aligned to the frequency with which those events occur. Students view their time and effort as capital; they are students’ most valued possessions, and how they allocate these commodities is a choice.
Practical implications
For students to become avid readers and writers, we must utilize a host of strategies to impress the notion that these activities are worth their attention, time, and investment.
Details
Keywords
Research serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the…
Abstract
Purpose
Research serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the extent to which research is adequate and appropriate in representing what it intends to measure and test. In this vein, this article aims to present a typology of validity to aid researchers in this endeavor.
Design/methodology/approach
Employing a synthesis approach informed by the 3Es of expertise, experience, and exposure, this article maintains a sharp focus on delineating the concept of validity and presenting its typology.
Findings
This article emphasizes the importance of validity and explains how and when different types of validity can be established. First and foremost, content validity and face validity are prerequisites assessed before data collection, whereas convergent validity and discriminant validity come into play during the evaluation of the measurement model post-data collection, while nomological validity and predictive validity are crucial in the evaluation of the structural model following the evaluation of the measurement model. Additionally, content, face, convergent and discriminant validity contribute to construct validity as they pertain to concept(s), while nomological and predictive validity contribute to criterion validity as they relate to relationship(s). Last but not least, content and face validity are established by humans, thereby contributing to the assessment of substantive significance, whereas convergent, discriminant, nomological and predictive validity are established by statistics, thereby contributing to the assessment of statistical significance.
Originality/value
This article contributes to a deeper understanding of validity’s multifaceted nature in research, providing a practical guide for its application across various research stages.