tends to show that products that are highly profitable and add to diversification are difficult to reach, while easily reachable products offer limited scope for diversification and growth.

A PS analysis does not provide answers to questions about future export winners for the focus country. However, on the basis of international experience (captured by detailed global trade data), PS analysis helps identify sectors that are attractive considering a country’s capabilities and the potential contributions of different sectors to diversification and income potential. The approach manages to accomplish this at a finely disaggregated sector level thanks to the fact that it is designed to make good use of a rich database for merchandise trade, with annual data and global coverage at the country level. However, it is important to be aware that its design and data constraints together exclude a wide range of issues from the purview of PS analysis. Among other things, PS analysis explains changes in export structure on the basis of sectoral proximities without reference to other issues that clearly matter (for example sectoral policies and human capital) but for which matching global databases do not exist. As a result, it is often advisable to combine or integrate PS analysis with other approaches that address some of these other aspects, albeit with limitations of their own. Such approaches may include expanded historical analysis that highlights the roles of policy and factor endowments in sectoral trade and production growth. In general, when envisaging the future roles of different sectors, it is important to draw on insights from sector experts and policy makers in the context of broader development objectives.

Among the data‐related shortcomings of most applications of PS analysis is that they do not address service sectors. In this paper, we try to mitigate this shortcoming by including a discussion of services, albeit not very detailed due to data limitations. Another shortcoming, which in practice probably is less serious, is related to the fact that the ability of different sector indicators to reflect country capabilities is hampered by the presence of high sector‐level subsidies in some countries.

# **4 The Case of Morocco**

In this section, the tools of PS analysis are applied to the case of Morocco. The analysis uses detailed export data starting in 1980 and examines patterns of changes in RCA and technology content (Section 4.1), and how the country’s export structure has positioned Morocco in PS and prepared the country for future growth (Section 4.2). Based on the country’s current capabilities, measured by the PS concept of _density_ , we construct a _naïve_ scenario of Morocco’s aggregate exports up to 2025. Finally, we discuss some important sector priorities (Section 4.3) informed by PS metrics.

## **4.1 Evolution of Morocco’s exports in recent decades**

Figure 1 summarizes the evolution of Morocco’s merchandise exports from 1980‐2010, with observations in five‐year intervals, according to patterns of RCA shifts.<sup>5</sup> As can be seen, 5 Following previous World Bank applications, we classified Moroccan merchandise exports according to long‐term shifts in revealed comparative advantage (RCA) using two distant five‐year periods, 1980‐1984 and 2006‐2010, constructing a binary variable (D‐RCA) that takes the value of 1 only if a product has an RCA value greater than unity during five consecutive years in each period. As a result of this classification, products can be grouped using a 2x2 matrix – with 2 time‐periods and 2 values of the D‐RCA variable. These 6