Measuring patterns of specialization using trade in value added: the case of manufacturing in Italy

ABSTRACT

This article aims to contribute to the long-standing debate on the ‘anomaly’ of Italy’s specialization in manufacturing by providing fresh analysis based on new data. It offers: 1) a comprehensive survey of Italian comparative advantages measured in value-added terms and their evolution over time; 2) an international comparison of these advantages with Italy’s main competitors. Results show that the country’s traditional comparative advantages are substantially confirmed, a genuine capacity of the Italian domestic production factors to maintain added value in production with comparative advantage can be observed and new trends in terms of specialization and competitors are also highlighted, showing the influence of the international fragmentation of production on trade and specialization.

KEYWORDS:

• Comparative advantage
• patterns of specialization
• trade in value added
• manufacturing
• Italy
• input–output tables

JEL CLASSIFICATION:

• F10
• F14
• L23
• L60
• O57

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed here.

Notes

¹ A country’s value added consists of rewards of its production factors such as labour and capital.

² These include: The Global Trade Analysis Project (GTAP), the Asian International Input–Output Tables by IDE-JETRO, the TiVA database by OECD–WTO and the World Input-Output Database (WIOD).

³ For a description of the database, see Timmer (2012).

⁴ The EU-27 countries plus Turkey, Canada, USA, Mexico, Japan, Korea, Taiwan, Australia, Brazil, Russia, India, Indonesia and China.

⁵ We use the 2013 release of the dataset since the new one (which covers the period 2000–2014) is not directly comparable or matchable with the previous one. Including data for the 1995–1999 period, the 2013 release better detects the trade specialization dynamic due to incidence of the emerging economies, starting in the early 2000s and enables a better comparison with the literature in gross value, which at the end of the 1990s registers some changes in the Italian comparative advantages.

⁶ The RCA index takes the following form:

$\mathrm{R}\mathrm{C}{\mathrm{A}}_{\mathrm{i}\mathrm{j}}=\frac{\frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{{\mathrm{x}}_{\mathrm{w}\mathrm{j}}}}{\frac{{\mathrm{X}}_{\mathrm{i}}}{{\mathrm{X}}_{\mathrm{w}}}}\in \left[0,\mathrm{\infty }\right]$

where x_ij and x_wj are, respectively, the exports of the product (or sector) j from country i and the world exports $w$ of product j, whereas X_i e X_w are, respectively, the total exports of country i and the world total exports. A country is defined as being specialized in the export of sector j when RCA_ij ranges from 1 to infinity, ‘revealing’ a comparative advantage. Otherwise, from zero to 1, RCA_ij suggests a comparative disadvantage.

⁷ The ‘DVA in exports’ is the sum of five components: (I) the DVA in direct final goods exports; the DVA in intermediates exports (II) absorbed by direct importers or (III) re-exported to third countries; the DVA in intermediates that returns within the country of origin (IV) via final imports or (V) via intermediate imports. See Koopman, Wang, and Wei (2014) for the detailed algebraic derivation of their decomposition.

⁸ We computed the indexes for all the years in the WIOD 2013 release (see supplementary material) and verified that a 4-year interval is appropriate to get the main trends, given also the space constraints.