Women farmers and community well‐being under modeling uncertainty

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

We examine the association between woman farmers and community well‐being using U.S. county‐level data. We address modeling uncertainty around three measures of community well‐being by using a spatial Bayesian model averaging approach and find that a higher share of farms operated or owned by women in a county is associated with higher rates of new business formation, longer life expectancies, and lower poverty rates. The results are consistent with a growing literature that finds women business owners approach their businesses more holistically, with positive community spillovers.

Related collections

Most cited references 86

Record: found
Abstract: not found
Article: not found

Multimodel Inference: Understanding AIC and BIC in Model Selection

K. Burnham (2004)

0 comments Cited 1178 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

David Posada, Thomas Buckley, Jeffrey Thorne (2004)

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).