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Do Global Value Chains Offer Developing Countries Learning and Innovation Opportunities?

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Abstract

The role of emerging economies in the global economy via embeddedness in Global Value Chains (GVCs) is increasing, but their ability to become innovation leaders is less certain. The GVC approach stresses that the inter-firm linkages afforded by being part of a chain are crucial for transferring knowledge. However, their impact on the innovation performance of the developing country firms involved in these GVCs remains controversial and requires more research. The present study provides a systematic review of the literature on developing country GVCs to investigate the learning channels used by local firms, both within (firm level, collective level) and outside of these value chains (i.e. external sources of learning), and the extent to which this activity promotes innovation. We use cluster analysis to classify the cases identified in a literature review to propose a novel typology of local GVC innovators: (a) GVC-led Innovators that achieve high levels of innovation, relying mainly on sources of knowledge within the GVC; (b) Autonomous Innovators whose innovation activity is based on external sources of learning; (c) Marginal Innovators, which constitute the largest group and are characterized by low levels of innovativeness and some use of knowledge available within the GVCs, but scarce use of external sources.

Le rôle des économies émergentes dans l’économie mondiale via l’intégration dans les chaînes de valeur mondiales (CVM) est croissant, mais leur capacité à devenir des leaders de l’innovation est moins certaine. La présente étude propose une revue systématique de la littérature sur les CVM des pays en développement afin d’étudier les canaux d’apprentissage utilisés par les entreprises locales au sein (au niveau des entreprises et au niveau collectif) et en dehors (les sources externes d’apprentissage par exemple) de ces chaînes de valeur. L’article étudie également la mesure dans laquelle cette activité favorise l’innovation. Nous proposons une nouvelle typologie des innovateurs locaux des CVM: (a) les innovateurs dirigés par les CVM qui atteignent des niveaux élevés d’innovation, en s’appuyant principalement sur les sources de connaissances au sein de la CVM; (b) les innovateurs autonomes dont l’activité d’innovation est basée sur des sources d’apprentissage externes; (c) Les innovateurs marginaux, qui constituent le groupe le plus important et se caractérisent par un faible niveau d’innovation et une certaine utilisation des connaissances disponibles au sein des CVM, mais une utilisation limitée des sources externes.

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Acknowledgements

A previous version of this work was commissioned as a background paper for the UNIDO Industrial Development Report 2016 and was funded by UNU-MERIT. The authors benefitted from comments received in workshops organized by UNIDO in Vienna and Globelics in Cuba and they also thank two anonymous referees for their helpful suggestions.

Author information

Authors and Affiliations

  1. Department of Economics and Management, University of Padova, via del Santo 33, 35123, Padova, Italy

    Valentina De Marchi

  2. Department of Economics and Management, University of Pisa, Via Ridolfi 10, 56124, Pisa, Italy

    Elisa Giuliani

  3. Department of Social Sciences, University of Pavia, Strada Nuova 65, 27100, Pavia, Italy

    Roberta Rabellotti

  4. Department of Business and Management, Aalborg University, Frederikskaj 10B, 2450, Copenhagen, Denmark

    Roberta Rabellotti

Authors
  1. Valentina De Marchi
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  2. Elisa Giuliani
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  3. Roberta Rabellotti
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Corresponding author

Correspondence to Elisa Giuliani.

Appendix

Appendix

Table A-1: The GVCs cases analysed

Industry

Country

Methodology

Clusters

Article

Complex products manufacturing

 Aeronautics

Brazil

Qualitative

GVCI

Cafaggi et al (2012)

 Automotive

India

Quantitative

LI

Kumar and Subrahmanya (2010)

 Automotive

India

Qualitative

LI

Krishna et al (2012)

 Automotive

China

Qualitative

AI

Altenburg et al (2008)

 Automotive

China

Qualitative

LI

Hatani (2009) 

 Automotive

Argentina

Quantitative

LI

McDermott and Corredoira (2010)

 Automotive

India

Quantitative

LI

Kumaraswamy et al (2012)

 Electronics

China

Qualitative

GVCl

Sturgeon and Kawakami (2011)

 Electronics

China

Qualitative

AI

Altenburg et al (2008)

 Electronics

China

Quantitative

LI

Dennis Wei et al (2011)

 Electronics

India

Quantitative

LI

Krishna et al (2012)

 Electronics

Indonesia

Quantitative

GVCI

Kadarusman (2012)

 Electronics

Indonesia

Qualitative

GVCI

Kadarusman and Nadvi (2013)

 Electronics

Malaysia

Qualitative

AI

Athukorala (2014)

 Electronics

Mexico

Qualitative

LI

Sturgeon and Kawakami (2011)

 Handset

China

Qualitative

GVCI

Brandt and Thun (2011)

 ICT

China

Quantitative

LI

Sun et al (2013)

 LCD Screens

China

Qualitative

AI

Yang (2014)

 PC

China

Qualitative

LI

Yang and Liao (2010)

 Pharmaceuticals

India

Quantitative

AI

Krishna et al (2012)

 Pharmaceuticals

Uganda

Qualitative

AI

Haakonsson (2009)

 Space Industry

India

Qualitative

AI

Altenburg et al (2008)

 Wind Turbine

China

Qualitative

AI

Lema et al (2013)

Traditional manufacturing

 Construction

Ghana

Quantitative

AI

Fu et al (2014)

 Diamond Cutting

Botswana

Qualitative

LI

Mbayi (2011)

 Food processing

Colombia

Qualitative

AI

Meléndez and Uribe (2012)

 Food Processing

Ghana

Quantitative

AI

Fu et al (2014)

 Football

China

Qualitative

AI

Nadvi (2011)

 Football

Pakistan

Qualitative

LI

Nadvi (2011)

 Footwear

Brazil

Qualitative

LI

Navas-Aleman (2011)

 Footwear

Vietnam

Quantitative

LI

Tencati et al (2008)

 Furniture

Brazil

Qualitative

LI

Navas-Aleman (2011)

 Garment

Indonesia

Qualitative

GVCI

Kadarusman and Nadvi (2013)

 Garment

Kenya

Quantitative

LI

Kaplinsky and Wamae (2010)

 Garment

Madagascar

Quantitative

LI

Kaplinsky and Wamae (2011)

 Garment

Vietnam

Qualitative

AI

Goto (2012)

 Garment and Textile

Ghana

Quantitative

AI

Fu et al (2014)

 Surgical Instrument

Pakistan

Quantitative

GVCI

Nadvi and Halder (2005)

 Textile

India

Qualitative

LI

Padmanand and Kurian (2014)

Natural resources

 Coffee

Brazil

Qualitative

GVCI

Cafaggi et al (2012)

 Floriculture

South Africa

Quantitative

GVCI

Matthee et al (2006)

 Fresh Fruits

Brazil

Qualitative

LI

de Castro Souza and Neto (2010)

 Seafood

Vietnam

Quantitative

LI

Tencati et al (2008)

 Vegetables

Guatemala

Qualitative

LI

Padilla-Perez (2014)

 Wine

South-Africa

Qualitative

LI

Ponte and Ewert (2009)

 Wood

Gabon

Qualitative

LI

Kaplinsky et al (2011)

 Wood

Thailand

Qualitative

LI

Kaplinsky et al (2011)

Services

 Film

South-Africa

Qualitative

LI

Barnard and Tuomi (2008)

 Software

India

Qualitative

AI

Altenburg et al (2008)

 Software

India

Qualitative

LI

Chaminade and Vang (2008)

  1. Legenda: GVCI: GVC-led Innovators; AI: Autonomous Innovators; LI: Marginal Innovators

Table A-2: Defining GVC groups

 

Innovation

(0–1)

Learning within the GVC

(0–1)

Learning outside the GVC

(0–1)

ANOVA

p-values

GVC-led Innovators

0.69

0.69

0.43

0.000

Autonomous Innovators

0.68

0.16

0.50

0.000

Marginal Innovators

0.33

0.27

0.14

0.000

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De Marchi, V., Giuliani, E. & Rabellotti, R. Do Global Value Chains Offer Developing Countries Learning and Innovation Opportunities?. Eur J Dev Res 30, 389–407 (2018). https://doi.org/10.1057/s41287-017-0126-z

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