approximation in most cases,<sup>17</sup> and the author’s view is that a value indicator would introduce significant additional noise. For the same reason, introducing a measure relative to GDP or population size would introduce the same source of distortion. An alternative could be a count variable of the number of FDI projects, but it would heavily bias the measure against small countries. The constructed binary variable is our dependent variable in the empirical analysis.

# II. <u>Trade data</u>

We use the BACI data set prepared by CEPII to construct a measure of the revealed comparative advantage at the disaggregated 4-digit sector level.<sup>18</sup> The BACI data set provides disaggregated data on bilateral trade flows for more than 5,000 products and 200 countries. The BACI database is built from data directly reported by each country to the United Nations’ COMTRADE database, where additional procedures are applied by CEPII to reconcile the declarations of the exporter and the importer and provide more accurate measurement of trade flows relative to COMTRADE. The data set is available at annual frequency. The 4-digit level of disaggregation was chosen because it offers a good compromise between data set size and use of results for the construction of the benchmarking values.<sup>19</sup> We use trade data to calculate the measure of the revealed comparative advantage (RCA) index (Belassa, 1967) for each country/sector pair. Based on the calculated RCA for each sector/country pair over the specified period, we construct the binary trade success variable which takes value 1 if the estimated RCA for country/sector pair is above 1 and 0 otherwise. We consider the use of a binary RCA variable as the most appropriate (though imperfect) approximation of whether a country has successfully exported a given product, relative to the rest of the world and export basket composition. The definition of export success in this case does not distinguish between instances of artificial competitiveness (for instance subsidized exports) and genuine ones, another limitation of the RCA and other trade outcome measures. Another downside of the indicator of success used in this analysis is that it is discrete despite being constructed from a data set with continuous and richer information. Using trade volumes or market shares would not be appropriate because it reflects the size of the exporting economy rather than specialization. Taking into account the RCA value or specific RCA levels in the variable would introduce information from this key variable that escapes easy interpretation: as it would pick up significant variations of RCA levels that can just as well be a reflection of the lack of economic diversification in other products rather than a reflection of success in the product in question on global markets. The constructed binary variable is our dependent variable in the empirical analysis.

# III. <u>Country indicators data</u>

The indicators of the characteristics of each economy are collected from World Bank TCdata360, complemented by IMF WEO data for several macroeconomic indicators. Indicators are grouped in 4 broad categories: demand, production factors, key inputs and institutions. The latter three categories are further decomposed into subcategories summarized in Table 1. These indicators were selected to correspond to the country characteristics a potential investor or trader might consider when targeting a

> 17 Authors observed that this data seems often informed by public announcement of investments. Realized investments often differ quite considerably in scope. This was confirmed in discussions with World Bank colleagues working with this data.

> 18 <u>http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=37</u>

> 19 The empirical investigation could be conducted at a more disaggregated level, e.g. HS-6 digit.

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