The binary matrix M sp of the year 2015. Each row of the matrix represents a Brazilian state. States are ordered in terms of their Fitness from the smallest value (row 0) to the largest one (row 26). Analogously columns represent Products ordered in terms of their Complexity from the smallest value (column 0) to the largest one (column 1172). The matrix elements M sp are drawn in dark green and the others in white. In the figure we highlight high Fitness states such as São Paulo and Paraná, a middle rank State such as Ceará and a low Fitness state such as Roraima. https://doi.org/10.1371/journal.pone.0197616.g001 

The binary matrix M sp of the year 2015. Each row of the matrix represents a Brazilian state. States are ordered in terms of their Fitness from the smallest value (row 0) to the largest one (row 26). Analogously columns represent Products ordered in terms of their Complexity from the smallest value (column 0) to the largest one (column 1172). The matrix elements M sp are drawn in dark green and the others in white. In the figure we highlight high Fitness states such as São Paulo and Paraná, a middle rank State such as Ceará and a low Fitness state such as Roraima. https://doi.org/10.1371/journal.pone.0197616.g001 

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In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-...

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... As the term of reference for the measure changes every year, changes in ECI over time do not have any longitudinal interpretation, as the scale with which the index is measured changes every year. In order to address this, the use of ECI (or other similar EC indicators) in longitudinal regressions techniques should rely on the projection of product complexity in a given year of the series, upon countries' Revealed Comparative advantages in every year (Sbardella et al., 2018a;Operti et al., 2018), or a measure that maintains an invariance of scale over time, as suggested by Mazzilli et al. (2022) and explained in section 2.2. ...
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Purpose-This study aims to systematically review the economic complexity literature to advance the knowledge on its contribution to building regional competitiveness. Design/methodology/approach-In this study, we did a systematic review of 111 relevant papers. In this regard, we did a thematic analysis on all the collected papers, which led to a two-level processed approach. In the first level, the contributions of the reviewed articles have been classified into three main streams. In the second level, the findings under each contribution category are analyzed and explained. This approach led to a thematic network demonstrating economic complexity and the dynamics of regional competitiveness and a set of managerial and policy implications. We followed a multiple processed approach for the systematic review of 95 papers that reveals considerable contributions in three categories, including measurement techniques, criticisms and exploratory studies. Findings-Despite some critiques and the undertaken evolution in measurement techniques of complexity, economic complexity has become a well-known method mainly for regions' competitiveness dynamics. Our review demonstrates a nested network of economic complexity dynamics that drives policy advice concerning countries' status in their development path. The provided set of policies includes guidelines for underdeveloped and developing countries and general policy implications, applicable for all regional contexts for building competitiveness dynamics. Originality/value-This research contributes to the literature on competitiveness from the window of economic complexity. The study allows a deep understanding of regions' productive structure role in their development and competitiveness. A set of policies for building regional competitiveness is provided concerning the study's findings. The literature gaps are identified, and future research ideas are provided for using economic complexity methodologically and logically to boost regional competitiveness.
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