framework that provides insights to relate the aggregate indicators to microeconomic and macroeconomic constraints of supply-side export performance or competitiveness).

In Section 2.1, we discuss the methodology of the analysis and how this relates to similar methodologies. In Section 2.2 we describe the scope of the resulting database and how this database relates to existing databases discussing and providing indicators of competitiveness.

# **2.1. Methodology and relation to other methods**

The Export Competitiveness Database resulting from this work encompasses quarterly information on year-on-year export growth from 2006q1 to 2013q1 for a total of 228 countries and territories worldwide, broadly representing all regions and income groups in the world. It is based on monthly and quarterly data available for the period since 2005 at the HS 6-digit level (2002 classification) from Trade Map of the International Trade Centre (ITC).<sup>2</sup> These are bilateral trade data covering the majority of countries and territories worldwide and 5,300 products of the Harmonized System. Reporting is relatively timely as it allows having information up to three months earlier.

The method proposed here envisages the computation of measures of export performance, sectoral specialization and geographical specialization. We use regression analysis to decompose export growth of bilateral export data at the HS-6 digit product level of disaggregation and using high frequency data. Specifically, the method envisages a decomposition of export growth based on a weighted variance analysis (ANOVA) of bilateral export data, disaggregated by product and using high frequency data. The model identifies the export growth of each exporting country as if all exporters had the same geographical and sectoral specialization. This is important for export data, as export growth rates are affected by structural effects: exporters with strong positions in the most dynamic destination markets or specialized in high growth sectors benefit _ceteris paribus_ from stronger growth. With this methodology, exporter performance can be assessed assuming neutral geographic and sectoral specialization. As mentioned earlier, the computation consists of four main steps.

This “shift-share” decomposition is based on Jayet (1993), the first paper that used statistical methods for the structural analysis of geography effects. Cheptea, Gaulier & Zignago (2005), Bricongne, Fontagné, Gaulier, Taglioni and Vicard (2011) and Cheptea, Fontagné & Zignago (2012) provide contributions and refinements that make the method suitable for application to international trade.<sup>3</sup> The method developed in this paper harmonizes the various refinements from

> 2 Trade Map, International Trade Statistics, International Trade Centre, www.trademap.org/tradestat/Index.aspx. 3 Cheptea et al. (2005) employs the method to identify factors driving changes in world market shares for 88 countries during the period 1995-2002 using annual bilateral trade data at the HS 6-digit product-level. Along with the export competitiveness, they consider the geographical and sectoral countries’ initial position on different import markets and their capacity to adapt to shifts in the world economy. They find that the export performance of emerging countries was fully driven by competitiveness gains, despite an unfavorable specialization in slow growing products and sectors. Cheptea et al. (2012) use an updated version of the data set, covering the period 1995-2009, to decompose annual changes in market shares into structural effects (geographical and sectoral) and a performance effect. The growth rate of country i’s exports was computed as the logarithm of the Törnqvist index of the exports of each product k to each partner c. Authors focused on high tech goods and top range products, to better explain the European exports resilience, compared to US and Japan losses in these key segments of international competition. Bricongne et al (2011) applied a similar methodology to decompose the growth of French exports, computed using elementary midpoint growth rates and firm level data covering the period January 2008 to April 2009. Their novel contribution is to properly account for the extensive margin of trade. Previous methods only used the intensive margin of trade to measure competitiveness.

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