# **3 Product Space Methodology and Concepts**

The PS method is based on a set of empirical measures that are focused on the relatedness between different products and the income levels that are associated with exporters of individual products. Among these measures, the most central, proximity (with distance as its opposite) was discussed above. In country analyses based on PS concepts, alternative exports baskets may be evaluated. In this context, PS analysis suggests that a country that strives to use exports as a means of accelerating growth should take into account several key aspects. Among other things, such a strategy should consider the dynamics of revealed comparative advantage ( _RCA_ ) to identify traditional, emerging, and sluggish exporting sectors. In addition, the country should strive to diversify, increasing competitiveness in product categories that are within reach and that offer higher scope for further diversification – using PS terminology, such categories have relatively high values for _density_ and _PATH_ , respectively. Lastly, when assessing different products, it is important to consider their income potential ( _PRODY;_ defined on the basis of GDP per capita and the shares of a product in the export baskets of countries with RCA>1 in the product); products with a high _PRODY_ relative to the country’s _EXPY_ (defined on the basis of _PRODY_ data and the product export shares for the country) promise to bring about stronger growth in the country’s GDP per capita. (Definitions of key PS concepts are found in Annex 1.)<sup>4</sup> In the following analysis of Morocco, the PS methodology is applied in a stepwise process. Drawing on previous World Bank applications, products are categorized according to structural shifts in _RCA_ and ranked according to prospects for further diversification ( _PATH_ ) and income potential ( _PRODY_ ). Products with high _density_ , high _PATH_ , and high _PRODY_ are usually the best candidates for diversification, as they combine strong production capability, large scope for further diversification, and high income potential. An increase in the share of products with high _PRODY_ raises the income potential of the country’s export basket ( _EXPY_ ).

In general, there is a trade‐off between products with high _density_ , high _PATH_ , and high _PRODY_ , i.e. between products that are within reach (“low‐hanging fruit,” which often means doing things that are only slightly different; the case of high _density_ ), products that offer greater diversification potential (often new products that require non‐marginal start‐ up investments; the high _PATH_ category), and products that raise potential income (often those for which it is most difficult to become an efficient producer; products with high _PRODY_ ). Empirically, the relationships are often negative between _density_ and _PRODY_ and between _density_ and _PATH_ , but positive between _PRODY_ and _PATH_ . In other words, _PS_ analysis often points to the difficulty of generating a successful development strategy as it 4 Further developing the PS framework, Hausmann et al. (2013) propose two aggregate measures of embedded knowledge: for societies, the Economic Complexity Index ( _ECI_ ) and, for products, the Product Complexity Index ( _PCI_ ). These indexes are constructed using an iterative method that, from global trade data, gathers information contained in the number of products that a country can make (the diversity of the country) and the number of countries that can make a product (the ubiquity of the product). The authors find that, controlling for per capita income, countries with a higher _ECI_ tend to grow faster in subsequent periods. While _ECI_ and _PCI_ have certain advantages, _EXPY_ and _PRODY_ are more transparent and fully adequate for our purposes. In fact, the correlation between ECI and _EXPY_ is .61, between _PCI_ and _PRODY_ is 0.79, and between _PCI_ and _PATH_ is .48.

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