_Source:_ Authors’ calculations based on World Bank-Burning Glass online Malaysia job postings data (2016-18). The means and ranges for the main skills categories (e.g. digital or cognitive) are higher than the statistics for the subsets (e.g. advanced or basic) since an occupation may require skills from more than one subset within a main skills category.

The average relative frequency of digital, cognitive, and socioemotional skills is in the range of 8-13 percent. The relative frequency of digital, cognitive, and socioemotional skills is similar (8-13 percent) while nearly three quarters of skills are classified other skills. Basic digital skills have a higher relative frequency than intermediate or advanced digital skills, though occupations requiring intermediate or advanced digital skills will implicitly require basic digital skills, as well.

When ranking occupations by their digital skill level, the ordering is similar to a ranking of these occupations based on the O*NET digital skill level, but with significant variance. In other words, our measure of the demand for digital skills in occupations generally tracks, but differs from, the O*NET measures. Thus, our estimates of the digital intensity of occupations may return results that are closer to the Malaysian – and perhaps the Southeast Asian – reality than the O*Net would have allowed.<sup>19</sup>

# _Defining occupation digital levels_

We use cluster analysis to assign each occupation to a digital occupation level. The cluster analysis allows the data to endogenously sort the occupations into groups with similar digital skills profiles. Since cluster analysis is an unsupervised machine learning technique, informed selection of the variables must be done to increase the likelihood of the clustering model to generate meaningful groups. We use the 13 variables as input to the model.

Five “raw” variables are included. The values for the “share of advanced, intermediate, and basic digital skills” (1) are the fundamental characteristics that encode an occupation’s digital level. The sum of these three, i.e., the “total share of digital skills" (2), provides a summary of how much digital skills is required by an occupation across levels of digital skills complexity. We also include three variables that represent the share of combinations of digital skills levels: advanced and intermediate digital skills share (3), advanced and basic digital skills share (4), and intermediate and basic digital skills share (5). Notably, all 127 occupations have at least 1 percent of their skills that are digital skills.

Several derived variables are included to increase the complexity of the model.<sup>20</sup> In particular, the ratio of a level of the digital skill share (i.e., the ratio of advanced digital skills to the total share of digital skills) intuitively provides a quantitative measure of the intensity of the digital skill (e.g., advanced)

> 19 It was not possible to directly compare the digital scores and occupational ranking by digital scores using the Malaysia data to that used by O*Net due to differences in methodology for generating digital scores between the two data sets. 20 The complexity of the model, in this context, pertains to the additional information that is used to fit the model. For example, a regression model with 100 variables is more complex than another regression model that uses only 10 variables.

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