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This paper explores the variation in inequality as a measure of convergence or divergence in per capita income. It is proposed to decompose this variation into different selected categories of foreign trade, especially due to technological intensity and intraregional and interregional trade. The first category of selection helps to understand how the different stages of development of foreign trade are associated with the convergence of per capita income between nations. The second perception shows whether regional integration actions have had any effect on the region's convergence. As a result, there is evidence of absolute convergence for the sample of Latin American countries and territories selected in the 1995–2017 period. The convergence was on the order of 16.7% and became more intense after 2004; however, it decelerated in the most recent period which is characterized by a phase of lower growth. The important participation of foreign trade in determining regional asymmetries in Latin America is confirmed and a significant effect of intraregional trade in the sense of reducing inequality. [ABSTRACT FROM AUTHOR]

As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are "good" or "bad." This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like "season" or "variability," and that simple decompositions can be combined to represent more complex concepts, like "seasonal variability," creating an expressive language through which to interrogate models and data. Plain Language Summary: Models are essential scientific tools for explaining and predicting phenomena ranging from weather and climate, to health outcomes, to economic development, to the origins of the universe, and testing competing models is one of the most basic scientific activites. Yet, how scientists evaluate and justify their models can be inconsistent or even arbitrary. Traditionally, one performance metric—such as mean squared error—is used to identify the best model, but one metric provides little insight into what aspects of a model are "good" or "bad." This paper proposes a basic language for expressing different aspects of a model's performance. On one hand, this is useful for determining which aspects of model may require revision, but it also allows the modeler to separate out the best elements among several models and combine them to form an ensemble, analogous to how an audio engineer mixes together multiple tracks to form the best rendition of a musical piece. Key Points: Mean squared error (MSE) is an objective but somewhat enigmatic measure of model performanceMSE can be decomposed into components that quantify specific aspects of model performance, such as bias and varianceMixing components among models yields a form of ensemble prediction [ABSTRACT FROM AUTHOR]