Evolution of the Productive Capabilities of Mexico: Economic Complexity Analysis for the Development of Special Economic Zones (SEZ)

ABSTRACT

This article aims to identify the capabilities that can enhance structural change at the state level in Mexico. Specifically, we analyze the evolution of the productive capabilities from 1999 to 2014 in the five less developed southern states where the federal government wants to generate development poles. Using the economic complexity methodology and estimating the network of relatedness, our findings indicate that these states have been specialized in non-complex capabilities, and that states specialized in capabilities with greater betweenness centrality tend to have greater diversification. Finally, we identify the manufacturing capabilities with the greatest potential to trigger structural change.

KEYWORDS:

• Special economic zones
• economic complexity
• regional development

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

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Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ Productive capabilities refer to tacit and useful knowledge embedded in people that allow them to perform a certain function (Hausmann et al. 2014).

² The complexity of an economy is related to the multiplicity of useful knowledge embedded in it and reflects the structure that emerges to hold and combine knowledge (Hausmann et al. 2014).

³ A SEZ is a geographical area delimited within the borders of a country where the business rules are different from those prevailing in the rest of the national territory. The differences in these rules refer mainly to the conditions of investment, international trade and customs, taxes, and regulations (Farole and Akinci 2011).

⁴ The NI level refers to a 6-digit code of the North American Industry Classification System 2013 (NAICS 2013) that allows us to identify not only the economic activities related with it, but the productive capabilities, in terms of knowledge and know-how, that they require to carry them out.

⁵ The productive capabilities space refers to the network of relatedness between productive capabilities.

⁶ In this study, we prioritize the productive capabilities related to the manufacturing sector given the international evidence about the strong relationship between this sector and the industrial policy of SEZ.

⁷ Following the economic complexity approach, from the TEP data, we can infer the productive capabilities, i.e., knowledge and know-how, that people possess. That is, if there are people employed in a certain NI, it is because they possess the capabilities required to carry them out.

⁸ Although the Economic Censuses were collected based on different NAICS, we used the conversion tables published by INEGI to standardize the NI codes with the most recent classification of 2013.

⁹ Where 32 is the number of states in the country and 818 is the number of NI.

¹⁰ Where 32 is the number of states in the country and 851 is the number of NI.

¹¹ Where 32 is the number of states in the country and 880 is the number of NI.

¹² Where 32 is the number of states in the country and 883 is the number of NI.

¹³ Having personnel employed in a certain NI does not mean that the state is specialized in the capabilities related with that NI. To be specialized, the entity must have “sufficient” staff engaged in the activity, where “sufficient” is given by the revealed comparative advantage threshold defined in this section.

¹⁴ The ubiquity is the number of states that are specialized in each NI. In this methodology, to calculate proximity, we use the maximum ubiquity of the two NI to minimize false positives. For more information, see the online Appendix which can be found online at www.tandfonline.com/uitj.

¹⁵ The matrix is symmetric, and the values in the main diagonal are ones because the proximity of each NI with itself is one.

¹⁶ Consider three products: grapes, wines, and auto parts. When calculating its proximity, it would be expected that between grapes and wines would be of greater proximity, closer to one, given that the productive capabilities needed (including capital, institutions, etc.), have greater similarity.

¹⁷ The methodology of these metrics is described in the online Appendix.

¹⁸ Through betweenness centrality (BC), we identify the NI that offer the most direct route between the clusters disconnected from the network. For more details on this metric, see the online Appendix.