Elizabeth A. Yeager

Productivity Divergence across Kansas Farms

This study used 30 years of continuous data for 135 farms in Kansas to explore changes in productivity using Malmquist productivity indices (MPI). The indices were used to determine whether there was productivity convergence or divergence in Kansas farms. The results showed there was significant divergence among the farms. The average annual productivity growth was 0.50 percent; the top farms based on MPI were larger in terms of value of farm production, crop farm income, and livestock farm income and received a larger percentage of their income from oilseeds, feed grains, and swine than the other farms on average.

Economic efficiency and downside risk

This article investigates the impact of downside risk on cost and revenue efficiency (RE) for a sample of farms. Downside risk or loss below a certain level of return is a concern regardless of producer risk preferences and thus a suitable measure of risk to use. Downside risk was measured as the weighted summation of net farm income below the amount needed for unpaid labour during the previous 10 years. Cost and RE were estimated using traditional input and output measures, and then re-estimated including each farm’s downside risk. Comparisons were made between the efficient farms with and without downside risk and the average for all farms. As expected, downside risk plays an important role in explaining farm inefficiency. Failure to account for downside risk overstates inefficiency and can lead to unrealistic expectations in potential efficiency improvements.

Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data

ABSTRACT The logistic regression model has been widely used in the social and natural sciences and results from studies using this model can have significant policy impacts. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this article is to examine the impact of alternative data sets on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model with observational data. A number of simulations are conducted examining the impact of sample size, nonlinear predictors, and multicollinearity on substantive inferences (e.g. odds ratios, marginal effects) when using logistic regression models. Findings suggest that small sample size can negatively affect the quality of parameter estimates and inferences in the presence of rare events, multicollinearity, and nonlinear predictor functions, but marginal effects estimates are relatively more robust to sample size.

Identifying Wildlife Species Believed to be Deserving of Protection From Hunting by U.S. Residents

ABSTRACT This article examines the perceptions of individuals toward protecting animals from hunting. Researchers surveyed 825 U.S. residents in an online survey about their views of whether 17 species of mammals should be protected from hunting. Over 85% of respondents believed elephant, white rhino, black rhino, hippo, leopard, lion, and polar bear species should be protected from hunting. Conversely, only 55% of respondents believed mountain lion and coyote should be protected. Cross tabulations and logit analysis were employed to explore relationships between believing an animal species should be protected from hunting and demographics. Older and female respondents more often agreed that species should be protected from hunting. Those who hunted or knew a hunter less frequently agreed that the species surveyed should be protected from hunting. Demographics and previous exposure to hunting appear to influence beliefs about what species should be protected from hunting.