have higher turnover (and thus vacancies) than others. Second, online job postings mostly target more skilled occupations, often published by formal firms looking for skilled workers who are comfortable using online sites for job search. Third, low-skilled work in South-East Asia frequently consists of selfemployed jobs that would not appear on online job search websites (Cunningham 2018).

The selectivity of vacancies is not an issue for the purpose of our study as long as there are a sufficient number of vacancies for a given occupation to construct a skills profile. Since we are not analyzing dynamics of vacancies but only using them to define skills requirements, the relative magnitude of the number of vacancies by occupation is irrelevant. Instead, we only need enough information to capture a representative picture of the skills profile for each occupation.<sup>12</sup>

# _Building skills categories_

We categorize the 8,221 skills in the World Bank-Burning Glass Malaysia data set into our three main skills categories of interest (digital, cognitive, and socioemotional) and nine subsets of the skills categories. Skills that do not fit these three categories are defined as “other technical and language skills.” We categorize the skills manually by assigning the 633 skills clusters to each skills category. When all skills within a cluster belong to one category, that cluster is assigned to the respective category. If the individual skills within a skill cluster cannot be assigned to one category, we assign each skill within the cluster to the appropriate category.<sup>13</sup> Skill clusters that are left unclassified by Burning Glass, as is the case for about a third of skills (2,592), are assigned manually, facilitated by a maximum likelihood method to estimate most likely skills clusters and cluster family.<sup>14</sup> About a third of the total skills in the Burning Glass Malaysia data are digital, cognitive, or socioemotional according to our classification. The 20 most frequently cited skills are a mix of cognitive (6), socioemotional (4), and other (7) skills, as well as three basic digital skills (computer literacy, Microsoft Office, and Excel) (Figure 3). We identify 2,622 unique digital skills: 24 are basic digital skills, 794 are intermediate digital skills, and 1,804 are advanced digital skills. Among the nondigital skills, 24 are cognitive and 29 are socioemotional. The remaining two-thirds of skills are

> 12 We acknowledge that the skills listed in vacancies may diverge from those actually employed by workers in firms; indeed, enterprise surveys from the region show large gaps between employers’ notional skills demands and workers’ supply of skills – digital and otherwise – in local labor markets (Cirera et al. 2021).

> 13 To assist with the categorization of digital skills, we also use the O*NET-based methodology for calculating digital occupation scores in Muro et al. (2017). However, we only use this measure as an initial categorization, followed by a manual review the categorization of every skill.

> 14 To infer the cluster and cluster family for the 2,592 skills that were not assigned to a cluster by Burning Glass, we implemented a nearest neighbor search of similar skills with available information to the skills with missing information. For each skill with missing information, we assign the most common cluster and cluster family of the top 30 skills similar to it. Similarity is defined as the cosine similarity of the vectors representing the skills. The skills are assigned a 100-dimensional vector by training a Word2Vec model where we represent the job postings as “documents” and the skills as the “words” within the document. The Word2Vec model learns semantic relationships between skills as they co-occur across job posts and embed this information into their respective vectors.

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