Publication bias in simulation model studies: The case of ethanol literature.

In this study, we explore the potential for publication bias using market simulation results that estimate the effect of US ethanol expansion on corn prices. We provide a new test of whether the publication process routes market simulation results into one of the following two narratives: food-versus-fuel or greenhouse gas (GHG) emissions. Our research question is whether model results with either high price or large land impact are favored for publication in one body of literature or the other. In other words, a model that generates larger price effects might be more readily published in the food-versus-fuel literature while a model that generates larger land use change and GHG emissions might find a home in the GHG emission literature. We develop a test for publication bias based on matching narrative and normalized price effects from simulated market models. As such, our approach differs from past studies of publication bias that typically focus on statistically estimated parameters. This focus could have broad implications: if in the future more studies assess publication bias of quantitative results that are not statistically estimated parameters, then important inferences about publication bias could be drawn. More specifically, such a body of literature could explore the potential that practices common in either statistical methods or other methods tend to encourage or deter publication bias. Turning back to the present case, our findings in this study do not detect a relationship between food-versus-fuel or GHG narrative orientation and corn price effects. The results are relevant to debates about biofuel impacts and our approach can inform the publication bias literature more generally.

Women's empowerment in agriculture and productivity change: The case of Bangladesh rice farms.

Using productivity change as a measure of farm economic performance, we analyze the relationship between women's empowerment in agriculture and farm productivity change and its components, which include efficiency change, technological change, and scale efficiency change. A non-parametric Malmquist approach is used to measure farm specific productivity change and its decomposition. We use a bootstrap regression to analyze factors that cause differences in productivity change and its components, testing, in particular, the role women's empowerment plays. The empirical application focuses on a sample of Bangladesh rice farms over the crop cultivation period 2011 and 2014. Results suggest that improvements in women's empowerment in agriculture were associated with higher levels of productivity change, efficiency change, and technical change, while they had no impact on scale efficiency change. We find that empowering women, specifically, improving their ability to make independent choices regarding agricultural production had a statistically significant positive association with productivity change, efficiency change, and technical change. We also find that lowering the gender parity gap is positively related with improving productivity of the sample farms.