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Digital Technologies and Product Upgrading in Global Value Chains: Empirical Evidence from Indian Manufacturing Firms

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Abstract

This paper provides empirical evidence on the impact of digitalisation on product upgrading in Global Value Chains (GVCs). Analysis is done for a sample of Indian manufacturing GVC firms in the period 2001–2015 from the firm-level database Prowess, using the methodology of System Generalised Method of Moments. Product upgrading is captured by a novel sales-weighted average product sophistication indicator constructed at the firm level. Digitalisation is captured through a digital capability index, constructed using principal component analysis, which draws information on both ‘hard’ and ‘soft’ digital assets for firms. Empirical results indicate that an increase in digital capability of an Indian GVC firm has a significant and positive impact on its product sophistication, implying that by investing in digital capabilities, Indian manufacturing firms can produce better and more sophisticated products in GVCs, enabling them to upgrade and climb up the value-chain ladder. Firms that are Digital Leaders produce 4–5% more sophisticated goods than Digital Laggards.

Résumé

Cet article fournit des preuves empiriques de l'impact de la numérisation sur la modernisation des produits dans les chaînes de valeur mondiales (CVM). L'analyse est effectuée sur un échantillon de manufactures indiennes dans la CVM au cours de la période 2001-2015 à partir de la base de données d'entreprise Prowess, en utilisant la méthode des moments généralisés par système. La modernisation des produits est mesurée grâce à un nouvel indicateur de sophistication moyenne des produits pondéré par les ventes au niveau de l'entreprise. La numérisation est mesurée au moyen d'un indice de capacité numérique, construit à l'aide de l'analyse des composants principaux, qui tire des informations sur les actifs numériques à la fois «matériels» et «logiciels» des entreprises. Les résultats empiriques indiquent qu'une augmentation de la capacité numérique d'une entreprise indienne dans la CVM a un impact significatif et positif sur la sophistication de ses produits, ce qui implique qu'en investissant dans les capacités numériques, les manufactures indiennes peuvent produire des produits meilleurs et plus sophistiqués dans la CVM, ce qui leur permet de se moderniser et de gravir l’échelle de la chaîne de valeur. Les entreprises qui sont des leaders numériques produisent 4% à 5% de produits plus sophistiqués que les celles qui sont des retardataires en matière de numériques.

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Notes

  1. Important exceptions in the empirical work on product upgrading include Khandelwal (2010) and Khandelwal et al. (2013), which use both information on unit values and quantities to estimate product quality and Eck and Huber (2016) which uses product sophistication.

  2. Prowess reports on total exports of the firm; it does not distinguish between intermediate and final exports.

  3. Products that score high on prody sophistication indicator are also observed to have a high value (greater than 1) on the product complexity indicator, developed by Hidalgo and Hausmann (2009).

  4. Using GDP per capita PPP allows for correction of differences across time (inflation) and across countries (deviations from PPP). This means that the product sophistication indicators can be compared across time and countries.

  5. Since the exporting countries and comparative advantages can change over time, calculating product sophistication using different countries in different years can create biases in the indicator (Hausmann et al. 2007). Therefore, it is important to create sophistication indices using data from a consistent sample of countries that report trade data in the period 2001–2015. This means exclusion of those countries for which trade data are missing for even one period in 2001–15. Consistent data are obtained for 113 countries, reporting both export flows and GDP in the period considered.

  6. Developed by Pearson (1901), PCA is a statistical technique to reduce dimensionality of multivariate data, widely used in literatures from many disciplines (Jolliffe and Cadima 2016). To construct the digital capability index, Kaiser’s rule—retaining components with eigenvector greater than 1—is followed and component 1 is chosen, which explains about 56 per cent of variation in the variables. Weights obtained from component 1 (0.55 for software assets, 0.65 for communication and transport infrastructure assets, and 0.54 for technology assets) are then used to construct the weighted digital capability index for every firm.

  7. This option subtracts the average of all available future observations, rather than subtracting the previous observation from the current one.

  8. Prowess data do not allow one to analyse entry and exit. However, data in Prowess mostly come from medium to large firms, therefore missing data for a firm is most likely due to the fact that the firm has not reported the data rather than it has exited the industry. The paper uses an unbalanced panel in which sample size varies from year to year, with only data availability and purging of outliers guiding our sample selection.

  9. It is interesting to note that these firm categories are closely related to Gereffi et al.’s (2005) GVC governance categories based on three key indicators: complexity of transactions, codifiability of information, and supplier competence. When information is complex and codifiability is low, suppliers will require a more skilled workforce to de-codify transactions (Lakhani et al.’s 2013), indicating an inverse relation between share of skilled labour and codifiability. Supplier competence—the ability of the supplier to interact with foreign buyers, to receive orders, and to fulfil requirements—is likely to be positively correlated with the digital capability index. From the governance perspective, the results in Table 6 therefore indicate that Indian firms linked in captive-type GVC governance structures (suppliers with overall low digital competence) are producing significantly less-sophisticated goods than relational suppliers (suppliers with high digital competence).

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Acknowledgements

This study was originally published under the UNU-WIDER project “Structural transformation—old and new paths to economic development.” The author extends thanks to Christopher Foster, Jostein Hauge and participants at the UNIDO and SASE workshops for feedback and comments.

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    Karishma Banga

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Appendices

Appendices

Appendix 1: Matching procedure

Each product in Prowess is defined by a unique 14-digit product code (PRID). Following Barrows and Ollivier (2016), each PRID is standardised to a product name by considering the number of times a product name is reported for a particular code and the product names reported for the PRID code at a more disaggregated level. The data are then cleaned, whereby all the products that are not classified under the manufacturing sector in Prowess data are removed. This involves removing products classified by Prowess as animal products, agricultural products, services, construction, and irrigation. After data cleaning, 2,977 unique 14-digit product codes are obtained. These product codes are then matched to the HS four-digit classification from WITS, using product names and numerical ordering. Both classifications have similar names and ordering, allowing 80% of the products in Prowess to be matched with four-digit HS classification (HS 1996). For example, in Prowess the PRID 6070101000000 refers to ‘Men’s overcoats etc., knitted or crocheted’, followed by the PRID 6070102000000—‘Women’s overcoats, knitted or crocheted’. These names are very easily matched to HS code 6101 ‘Men’s or boys’ overcoats, car-coats, etc. knitted or crocheted’ and HS 6102 ‘Women’s or girls’ overcoats, car-coats, knitted or crocheted’, respectively. See Banga (2017) for more details on matching.

Appendix 2

See Table 1.

Table 1 Construction of control variables used in regression analysis
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Appendix 3

See Table 2.

Table 2 Manufacturing industries for analysis
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Appendix 4

See Table 3.

Table 3 Summary statistics for GVC panel, 2001–2015
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Appendix 5

See Fig. 1.

Fig. 1
figure 1

Source author, based on Prowess data

Product sophistication index in GVC firms.

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Appendix 6

See Table 4.

Table 4 Dependent variable: firm-level product sophistication (PSit)
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Appendix 7

See Table 5.

Table 5 Robustness checks: dependent variable: firm-level product sophistication
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Appendix 8

See Table 6.

Table 6 Dependent variable: firm-level product sophistication (PSit)
Full size table

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Banga, K. Digital Technologies and Product Upgrading in Global Value Chains: Empirical Evidence from Indian Manufacturing Firms. Eur J Dev Res 34, 77–102 (2022). https://doi.org/10.1057/s41287-020-00357-x

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