# **3. Data**

We build a data set of occupational skills profiles for four Southeast Asian countries that we call the Southeast Asia Digital [SEAD] data set. The SEAD is created from online job postings data collected in Malaysia that we link to employment data from household and labor force surveys in Malaysia, Cambodia, Thailand, and Vietnam. The online job postings data are used to identify the types of skills (digital, cognitive, socioemotional, and others) associated with each occupation in Malaysia. These occupational requirements are then extrapolated to the employed population of Malaysia and the three other countries selected because of similar interests in digital development, potential synergies from being in the same sub-region, and the availability of data.<sup>5</sup> The household and labor force surveys provide information on the distribution of employment by occupation in each country.

## **3.1. Deriving skills requirements from online job postings data**

_The World Bank-Burning Glass Online Job Postings data in Malaysia_ We begin with a data set of more than half a million online job postings collected by the firm Burning Glass Technologies (Burning Glass hereafter) in Malaysia from May 2016 to December 2018.<sup>6</sup> In collaboration with the World Bank, Burning Glass collected more than 600,000 unique online job postings.<sup>7</sup> More than 95 percent of vacancies listed at least one skill requirement for a total of 8,221 unique skills. The raw data, in the form of job postings, include the job title, a free-text description field that lists required skills, and other job characteristics. Burning Glass parsed the text of each job vacancy, coded keywords and phrases as skills, and categorized the job titles into the 2013 Malaysia Standard Classification of Occupations (MASCO).<sup>8</sup> Burning Glass grouped individual skills into 633 clusters (groups of similar skills commonly learned together or substitutable),<sup>9</sup> which were then further grouped into 28 cluster families that are roughly aligned with sector-occupations (Burning Glass 2019).

The World Bank-Burning Glass Malaysia Job Postings data is not representative of the distribution of jobs in Malaysia for two main reasons. First, the job postings collected by Burning Glass between 2016 and 2018 are a subset of all job vacancies during this period: they represent one-fifth of the number of

> 5 Cambodia and Vietnam are lower-middle income countries and Malaysia and Thailand are upper-middle income countries. 6 For recent studies using Burning Glass data for the U.S., see Deming and Kahn (2018), Acemoglu et al. (2020), and Deming and Noray (2020). For the U.S. and five other countries (Australia, Canada, New Zealand, Singapore, and the United Kingdom), see Cammeraat and Squicciarini (2021) and Squicciarini and Nachtigall (2021).

> 7 See appendix 6 of CSC (2019, pp. 126–134) for details on the online job postings data collected by Burning Glass in Malaysia. The data we use in this paper uses one additional month of vacancies (December 2018). The preliminary version of this data set was used to identify demand for occupations in Malaysia (CSC 2019).

> 8 Only 1.8 percent of postings could not be mapped to occupation codes and are thus excluded from the sample from which we draw skills requirements for occupations.

> 9 Similar skills commonly learned together include, for example, Microsoft Word and Excel in a course on Microsoft Office.

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