Revealed Comparative Advantage is an index used in international economics for calculating the relative advantage or disadvantage of a certain country in a certain class of goods or services as evidenced by trade flows[1].

RCA is equal to the proportion of a country’s exports for the product or product class under consideration, divided by the proportion of world exports for that product or product class.

A comparative advantage is said to be ‘revealed’ if RCA > 1.  If RCA is less than unity, the country is said to have a comparative disadvantage in the commodity or industry.

RCA – also referred to as the Balassa Index – is widely used in global trade, especially in the agriculture sector.   According to a 2010 study, for example, Brazil exported 22 times its ‘fair share’ of soybean exports (RCA = 22).  A more recent study calculated – perhaps surprisingly – that Spain has one of the highest RCA values for automobiles, along with (less surprisingly) Japan.

What does this have to do with our project? Well, the same idea can be used to find which skills are more important in certain occupations, relative to the working population as a whole.

A 2018 study by MIT[2] used the RCA calculation to identify overexpressed skills in occupations while filtering out ubiquitous skills that everyone uses. The study went on to construct a ‘skillscape’, which used skill complementarities – i.e. the conditional probabilities of pairs of skills being effectively used (their term for being ‘revealed’, having RCA > 1) – to visualise skills that tend to ‘go together’ in certain occupations.

At BMI, we consider RCA calculations as being a useful, though not exclusive, tool for taking first looks at occupational requirements and content and for identifying important job characteristics.

The first chart shows O*NET abilities, knowledge elements, skills and work activities that score more than their ‘fair share’ (in this example, RCA > 1.5) for Accountants. As we might expect, elements like number facility, economics and accounting, management of financial resources and interacting with computers all score highly.

When we use this same method on O*NET Intermediate Work Activities (IWAs)[3] the results can be even more striking. Because each occupation has only a subset of IWAs (the current release of O*NET has 332 IWAs and on average each occupation has around 20), we can use the RCA calculation to very quickly see the important ones.  The second chart illustrates this, again for Accountants – note the RCA score ranges relative to the ones for abilities etc. in the first chart.

In a following post we shall describe another important way that we can use RCAs, in the calculation of skill complementarities as used in the MIT skillscape described above.  At BMI, we use RCA and complementarity calculations as important analysis tools in the kind of work we are describing in in this series of blog posts on Insights through O*NET.


[1] Wikipedia definition at


[3] We discuss IWAs in our post at