Innovation Strategies Matter: Latin America’s Middle-Income Trap Meets China and Globalisation

Abstract

Productive transformation from commodity production to higher value added activities is at the heart of the transition from a middle-income to a high income economy. The key is the development of domestic innovation capabilities to move up the value chain on a broad enough basis to generate sustained productivity growth. Since WW II few countries have achieved this transition. Under the market-led strategies of the past 30–40 years, Latin American countries have become trapped at the middle-income level. Informed by a structural-evolutionary approach, I investigate the reasons for the poor productivity performance in the region. I analyse the ‘within’ and ‘across’ sector sources of productivity growth in nine Latin American countries over the period 1950–2011, compare it with China’s, and link the outcomes to public policy, both with respect to state-led and market-led strategies, and to specific policies aimed at advancing innovation. I argue that the current globalisation process, particularly the rise of China, have shifted the goal posts for middle-income countries and increased the urgency to develop domestic innovation capabilities.

Acknowledgements

I would like to thank the anonymous reviewers and Ken Shadlen for helpful feedback on previous drafts. Replication data are available to bona fide researchers on request.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. I follow the terminology suggested by Bértola and Ocampo (2012) of ‘state-led development’ rather than ISI (import-substituting industrialisation). It focuses on the critical role of government policies in support of industrialisation and provides an apt juxtaposition to the market-led strategy that followed.

2. Analysing the relationship between R&D intensity and GDP p.c. for the same 54 countries for which data were available in 2000 and 2014, we find that a $1,000 increase in income is associated with an increase in R&D intensity of .06 in 2000 and .04 in 2014. The R² was 0.5295 and 0.3788, respectively.

3. The data for total factor productivity (TFP) are equally discouraging. TFP increased from 1960 to the mid-1970s after which it started to decline. In 2005, the level of total factor productivity was lower than it had been in 1960 (Daude, 2010).

4. Following McMillan and Rodrik (2011) and others, I use employment shares for the base year and productivity levels for the final year. That results in a higher contribution of the ‘within-sector component’. If one uses base year productivity levels and employment shares in the final year, the ‘across-sector component’ would be relatively larger (De Vries, Timmer, & de Vries, 2013).

5. For details of the construction of the variables and database see Timmer, de Vries, and de Vries (2014).

6. The analysis in this paper is based on nine sectors. A number of Latin American countries do not report data for ‘government services’ separately, but include them under ‘community, social, and personal services’. I adjusted the data for all Latin American countries accordingly.

7. In 2016, the World Bank defined a high income country as one with a GNI p.c. greater than $12,235 (Atlas method). Chile’s GNI p.c. was 14,430 in that year.

8. Pagés (2010) distinguishes between the dynamic and static elements of the structural change component, while McMillan and Rodrik (2011) look at the combined effect only.

9. Chile had already adopted a MLD strategy after the military coup in 1973.

10. In the 1990s, mining output increased by 300 per cent due to new mines coming on line, while more labour-intensive coal mines in the south of the country were closed (Álvarez & Sutin, 2017).

11. Firm-level data are necessary to determine whether this was due to the exit of inefficient firms, the high rents in raw material processing during the commodity price boom, or other reasons.

12. Jenkins (2014, p. 415) provides a detailed breakdown of Brazil’s loss of market share by export destination and technology level of exports for 2004–2010.

13. The Global Innovation Index is compiled by Cornell University, INSEAD and the World Intellectual Property Organization (WIPO). The innovation input index is based on five pillars (institutions, human capital and research, infrastructure, market sophistication, and business sophistication), and the innovation output index is based on two pillars (knowledge and technology outputs, and creative outputs). Each pillar, in turn, is composed of several indicators.

14. For example, after the military coup in Argentina in 1976, the government actually dismantled the science and technology complex (Dutrénit & Arza, 2015).