Background: Regional Economics and Economic Growth focus on the question of whether trade leads to a greater concentration of economic activity. Nevertheless, little empirical work has assessed the impact of trade on regional convergence. Therefore, this paper studies the regional convergence that has occurred in Mexico since NAFTA came into effect. Unlike previous papers, working with municipal-level data allows us to both observe the convergence patterns across space more clearly and to identify the effect of NAFTA. Methods: A conditional β-convergence model and conditional club β-convergence model were performed. However, in contrast to previous empirical studies, we use municipal panel data to identify more clearly the relationship between trade and regional convergence, and include the 2009 economic census to observe whether, after fifteen years of NAFTA, the economy has decentralized away from Mexico City to the US border regions.

as a particular type of organization within the structure of the Higher Education System, have become significantly relevant in explaining some of the causes of the development achieved in different countries over the last years. The literature has focused on the efficiency levels of Higher Education Institutions (HEI), as well as on the main factors that might account for the calculated efficiency rates. In this regard, DEA has been one of the most widely used techniques to estimate efficiency and explain the influence of each input and output in obtaining the coefficient (Liu et al. 2013). One of the main reasons that explain the use of a nonparametric methodology in the analysis of efficiency in higher education institutions is related to the possibility of working with multiple outputs and multiple inputs simultaneously, in conjunction with the parametric methodologies traditionally employed in the study of efficiency (see Emrouznejad et al. 2008; Aristovnik and Obadić 2014). Additionally, the characteristics of the university system naturally lead to rely on a model that considers more than one output, since, apart from providing with higher education degrees to graduates, universities play an important role in scientific production, not only through the integration

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In this paper, Ciccone's () approach is applied to the Spanish case in 2011 but by estimating it using local labor markets (LLMs) instead of NUTS‐2 or NUTS‐3 regions. It is especially relevant in the case of Spain because the NUTS‐3 (provinces) are large regions in comparison with other cases in Europe. From a sample of income taxpayers published by the Spanish Fiscal Studies Institute, we derive figures on average wages by worker on the scale of LLMs. We argue that working at this level of spatial disaggregation is more in line with the idea of externalities from agglomerations, which are generated on a local scale. We can also observe intra‐regional heterogeneity and how the urban wage premium changes along the entire distribution of cities, including small‐medium size urban areas or rural areas. The empirical analysis is based on several estimation strategies, namely, ordinary least squares, two‐stages least squares, quantile regressions (QR), and instrumental variable quantile regressions (IVQR) estimators; they all find a significantly positive effect of agglomeration in the conditional mean of wages. This result can be estimated along the conditional distribution of wages. According to the QR and IVQR estimates, important variations are found along the distribution.

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The compilation of the information required to construct survey-based input-output (I-O) tables consumes resources and time to statistical agencies. Consequently, a number of non-survey techniques have been developed in the last decades to estimate I-O tables. These techniques usually depart from observable information on the row and column margins, and then the cells of the matrix are adjusted using as a priori information a matrix from a past period (updating) or an I-O table from the same time period (regionalization). This paper proposes the use of a composite cross-entropy approach that allows for introducing both types of a priori information. The suggested methodology is suitable to be applied only to matrices with semi-positive interior cells and margins. Numerical simulations and an empirical application are carried out, where an I-O table for the Euro Area is estimated with this method and the result is compared with the traditional projection techniques.

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