On the evolution of technological specialization patterns in emerging countries: comparing Asia and Latin America

ABSTRACT

This paper explores the role of technological opportunity and cumulativeness in the evolution of technological specialization patterns (TSP) in catching-up processes. Concretely, I assume that opportunity induces mobility while cumulativeness leads to diversification and stability. The empirical analysis uses patent data indicators for nine Asian and Latin American countries between 1978 and 2012. The paper shows that, during economic liberalization (although with different timings), emerging countries caught up and redefined the path of technological accumulation for Asian and Latin American countries. With the exception of Hong Kong, all the countries increased their technology share, but they ran in different directions. Asian countries made greater relative efforts in dynamic technologies, while Latin American countries focused on stagnant technologies. In this sense, Asian countries achieved a more successful performance, building new technological bases and taking a technological leap in some of the more dynamic technologies. Meanwhile, Latin American countries left their technological bases unchanged. The paper also shows that, at the beginning of the catching-up cycle, the TSPs were turbulent. Afterwards, cumulativeness in the technological choice induced the diversification and stabilization of the TSPs.

KEYWORDS:

• Technological opportunity
• cumulativeness
• technological specialization patterns
• emerging countries
• Asia
• Latin America

JEL CLASSIFICATION:

• O30
• O52
• O54

Acknowledgments

I am grateful to Frederico Rocha and to two anonymous referees from Economics of Innovation and New Technologies, whose comments and suggestions encouraged me to investigate the nature of the evolution of national technological specialization in depth.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. The methodology to classify competences combines the revealed technology advantage (RTA) by the j-country, defined as $\text{RT}{\text{A}}_j=c_{ij}/o_i$, and the relative innovative effort over a ‘medium value,’ c_ij being the technology share of the j-country in the i-technology and o_i the i-technology share in the world. As countries do not display activity for all 30 technical fields, the median of Florence (MF) – the technology share that cumulates 50% of the major efforts – represents the medium value. The core competences are those that take RTA > 1 and c_ij > MF; niche competences take RTA > 1 and c_ij < MF; background competences take RTA < 1 and c_ij > MF; and marginal competences take RTA < 1 and c_ij < MF.

2. There is no evidence of a significant ‘small-numbers’ effect on the results. The regressions show that the TSPs are more stable when the number of technologies is higher. However, the more turbulent countries are not those with fewer technologies in the initial period (1986–1990), the only period that could have a possible ‘small-numbers’ problem. For example, in the first regression, Mexico, Hong Kong, and Taiwan have the same β-parameter (0.40) but quite different numbers of technologies in 1980–1990: Mexico (159), Hong Kong (278), and Taiwan (603). China and South Korea also present similar turbulence (0.38 and 0.37, respectively), and South Korea has 654, twice China’s value (333) in the same period.