metadata: authors: 0: author: Cunningham,Wendy
1: author: Moroz,Harry Edmund
2: author: Muller,Noel
3: author: Solatorio,Aivin Vicquierra
count: South Asia
docna: 0: docna: The Demand for Digital and Complementary
            Skills in Southeast Asia
docty: Policy Research Working Paper
abstracts: cdata!: As the economies of Southeast Asia
            continue adopting digital technologies, policy makers
            increasingly ask how to prepare the workforce for emerging
            labor demands. However, little is known about the skills
            that workers need to adapt to these changes. Skills profiles
            in low- and middle-income countries are typically derived
            from data collected in the United States, which is known to
            inaccurately reflect their occupational skills. This paper
            uses online job postings data from Malaysia to identify the
            digital, cognitive, and socioemotional skills required for
            digital and non-digital occupations. The skills profiles for
            each occupation are then merged with labor force survey data
            from Cambodia, Malaysia, Thailand, and Vietnam to sketch
            skills profiles of the workforces in these countries. Using
            descriptive statistics and linear probability model
            regressions, the paper finds evidence that highly digital
            occupations require not only digital skills, but also
            cognitive and socioemotional skills. Similarly, virtually
            all occupations, regardless of the digital intensity of the
            job, require some basic or intermediate digital skills.
            Pairwise correlations and a factor analysis confirm the
            complementarity between digital skills and different subsets
            of cognitive and socioemotional skills. The data also
            confirm that, even with the excitement about the digital
            revolution, the bulk of employment in Southeast Asia is in
            low- (around two-thirds) or medium-digital (around
            one-third) occupations. Only between 1 and 5 percent of jobs
            are highly digital in the four countries studied. These
            findings suggest that as education and training systems
            adapt to teach basic digital skills, they will need to
            continue to foster cognitive and socioemotional skills.
display_title: The Demand for Digital and
            Complementary Skills in Southeast Asia
listing_relative_url: /research/2022/05/33833724/demand-digital-complementary-skills-southeast-asia
topicv3: Education m1327048 470,Social Protection and Labor m1327194 697,Jobs m1503931 2670
publishtoextweb_dt: 2022-05-31T00:00:00Z
dois: 10.1596/1813-9450-10070
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 145073
1: author_key: 1219375
2: author_key: 1241960
3: author_key: 1650629
model_extractions: Policy Research Working Paper 10070 The Demand for Digital and Complementary Skills in Southeast Asia _Wendy Cunningham Harry Moroz Noël Muller Aivin Solatorio_ Social Protection and Jobs Global Practice May 2022
Policy Research Working Paper 10070

# **Abstract**

As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes. Skills profiles in low- and middle-income countries are typically derived from data collected in the United States, which is known to inaccurately reflect their occupational skills. This paper uses online job postings data from Malaysia to identify the digital, cognitive, and socioemotional skills required for digital and non-digital occupations. The skills profiles for each occupation are then merged with labor force survey data from Cambodia, Malaysia, Thailand, and Vietnam to sketch skills profiles of the workforces in these countries. Using descriptive statistics and linear probability model regressions, the paper finds evidence that highly digital occupations require not only digital skills, but also cognitive and socioemotional skills. Similarly, virtually all occupations, regardless of the digital intensity of the job, require some basic or intermediate digital skills. Pairwise correlations and a factor analysis confirm the complementarity between digital skills and different subsets of cognitive and socioemotional skills. The data also confirm that, even with the excitement about the digital revolution, the bulk of employment in Southeast Asia is in low- (around two-thirds) or medium-digital (around onethird) occupations. Only between 1 and 5 percent of jobs are highly digital in the four countries studied. These findings suggest that as education and training systems adapt to teach basic digital skills, they will need to continue to foster cognitive and socioemotional skills.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wcunningham@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Demand for Digital and Complementary Skills in Southeast Asia**<sup>∗</sup>

Wendy Cunningham, Harry Moroz, Noël Muller, and Aivin Solatorio The World Bank

**JEL codes** : J21, J23, J24, J63.

**Keywords** : Digital skills, cognitive skills, socioemotional skills, employment, Cambodia, Malaysia, Thailand, Vietnam.

> ∗ We are grateful to Burning Glass Technologies for their assistance with data collection, to the Digital Development Partnership for financing this work, and to Victoria Levin, Andrew Mason, and Achim Schmillen for their feedback.
# **1. Introduction**

Digital technology has become common in the workplace. Nearly two-thirds of workers in the European Union reported using a computer or smartphone at work in 2015 (OECD 2019). Computer use at work is less widespread but still prevalent in low- and middle-income countries: 30 percent of respondents in 10 middle-income countries reported using computers at work in 2012-13, ranging from 34 percent in urban Vietnam to 55 percent in Yunnan, China (Saltiel 2020). Ride-hailing and digital payment apps have made smartphones an important tool in even the most elementary occupations, such as motorbike taxis and small shopkeepers, in developing countries around the world.

The COVID-19 pandemic accelerated the adoption of digital technologies in workplaces. In response to the pandemic, firms in low- and middle-income countries, including in Southeast Asia, increased their use of and investments in digital platforms and solutions, such as e-commerce technology and online payment systems (Apedo-Amah et al. 2021; DeStefano and Timmis _forthcoming,_ discussed in World Bank 2021). Firms looked for ways to facilitate remote work for workers who could do their jobs from home.

Despite these trends, there is limited evidence on the skills needed to accompany the increasing digitalization at work, especially in low- and middle-income countries. While Southeast Asian countries are anticipating an Industrial Revolution 4.0 driven by the rise of digital technologies, there is little information about how these technologies will change the task content and skills requirements of jobs. Demand for digital skills is almost certain to rise. However, the types of digital skills required, and the extent to which occupations outside of the information and communications technology (ICT) sector will require digital skills, remains unclear. Furthermore, there is limited information on the extent to which other crucial skills, specifically cognitive skills (general knowledge and mental abilities) and socioemotional skills (behaviors and attitudes to manage emotions, relationships and personal goals), are needed to learn and manage digital technologies required in the workplace.

Previous work using data from high-income countries provides evidence of the complementarity among digital, cognitive, and socioemotional skills. Research using the PIAAC surveys of adults in 30 countries (mostly high-income) shows that workers using more digital skills at work also have higher basic cognitive skills (e.g. numeracy) and perform more tasks that require cognitive and socioemotional skills, such as management and communication, accountancy and sales, and negotiation (OECD 2016, 2019). ICT occupations, such as software and application developers and database professionals, require more advanced digital skills (e.g. programming). However, the PIAAC data also show that workers in ICT occupations perform many tasks that require cognitive and 2
socioemotional skills (OECD 2019). Furthermore, workers in more digitally intensive sectors in those same countries earn higher wages when they have higher numeracy and self-organization skills (both cognitive skills) (Grundke et al. 2018). A similar result is found in the United States: using online job postings for professional occupations, Deming and Kahn (2018) find positive pairwise correlations of 0.5 between posted requirements for basic digital skills (computer literacy and office programs) and skills categories related to cognitive and socioemotional skills, as well as positive correlations of around 0.2 between advanced digital skills (programming and specialized software) and cognitive and socioemotional skills categories.<sup>1</sup> In this paper, we investigate the extent to which digital and non-digital (cognitive and socioemotional) skills are complements in digital and non-digital occupations and how these skills are distributed across the labor market in four Southeast Asian countries. Since detailed data on the skills requirements of occupations is not available for the study countries, we build a data set of skills requirements by occupation using data from more than half a million online job advertisements posted in Malaysia between 2016 and 2018. We classify the skills as digital, cognitive, socioemotional, and others. We use the resulting data set to create skills profiles at the occupation level, which we then match to employment data from nationally representative surveys in Cambodia, Malaysia, Thailand, and Vietnam. We define four levels of the digitalization of an occupation (very-low, low, medium, and high) based on the share of basic, intermediate, and advanced digital skills in each occupational profile. This allows us to answer two questions. First, we explore how digital, cognitive, and socioemotional skills correlate with each other within occupations, focusing on Malaysia where the primary source of data was collected. Second, we estimate the extent to which digital and nondigital skills are used in each country, given the occupational structure of the individual labor markets.

In addition to providing the answers to these questions, the data and methodology add value in two ways. First, this is the first paper to use data from a Southeast Asian country to construct occupational skills profiles that include cognitive, socioemotional, and digital skills. Most studies use the occupational skills profiles reported in the U.S. Occupational Information Network (O*NET).<sup>2</sup> However, O*NET tends to misrepresent the occupational skills in low- and middle-income countries due to differences in job tasks, skills, and production technologies across countries (Caunedo, Keller, and Shin 2021; Lewandowski et al. 2021; Lo Bello, Sanchez-Puerta, and Winkler 2019). The Malaysia

> 1 Deming and Kahn (2018) also find that people management (linked to supervision, leadership, non-project management, mentoring, staff), which can be classified as a socioemotional skill, has a much lower correlation with their measures of basic and advanced digital skills: 0.24 and -0.06, respectively (p. 351).

> 2 See for example Acemoglu and Autor (2011) for the U.S.; Goos, Manning, and Salomons (2009) for Western Europe; and Apella and Zunino (2018) for Latin American and Eastern and Central Europe countries.

3
data on occupational skills profiles is likely to proxy the other three Southeast Asian countries in the study more accurately than occupational skills profiles from the U.S. Second, the analysis identifies skills requirements from unstructured data – job postings – rather than from a predefined list of skills or tasks (as done in a firm survey, for example), allowing the data to “speak for itself” when creating skills categories.

The analysis has three conclusions. First, virtually the entire employed population in the four-country sample are in low- and medium-digital occupations. Around two-thirds of the employed population is in low-digital occupations, which means that their tasks require limited use of even the most basic digital technologies. Less than 3 percent work in highly digital occupations, which require frequent use of digital technologies.

Second, highly digital occupations require similar levels and kinds of cognitive and socioemotional skills as do low- and medium- digital occupations. Three exercises confirm this conclusion. Using data from Malaysia and regression analysis, we observe that low-, medium-, and highly digital occupations have different shares of digital skills but roughly the same shares of cognitive and socioemotional skills. The few exceptions are that medium- and high-digital occupations more frequently require thinking skills (one of the three cognitive skills subsets that we explore) and low-digital occupations more frequently require relationship skills (one of the three socioemotional skills subsets in our sample). Furthermore, pairwise correlations and an exploratory factor analysis confirm complementarities between levels of digital skills and subsets of cognitive and socioemotional skills. Similar analysis using occupational data from Cambodia, Thailand, and Vietnam show similar results.

Third, all occupations are digital to some degree. In Malaysia, the share of skills that are digital in very low-, low-, and medium-digital occupations are 5, 9, and 15 percent. At least 1 percent of skills in every occupation in Malaysia is digital, ranging from 1 percent for cooks to 58 percent for software and applications developers and analysts. Results emerging from Cambodia, Thailand, and Vietnam occupational data show similar orders of magnitude.

Overall, our findings show that the education and training sector will need to provide instruction that covers digital, cognitive, and socioemotional skills, regardless of the extent of technological change in the workplace. Since even low-digital occupations will need some level of digital skills, basic digital skills training is necessary for all.

4
# **2. Conceptual Framework and Definitions of Skills**

There are intuitive reasons why cognitive and socioemotional skills would be complements to digital skills. Cognitive skills linked to analytical and critical thinking help make sense of the information accessed through digital skills, say surfing the internet or analyzing data. Learning and problem solving can help to develop more digital skills and take advantage of complex computer processes. Socioemotional skills may help workers collaborate through digital technologies, use these technologies to produce creative outputs, and adapt as technologies evolve.<sup>3</sup> As such, we can think of cognitive and socioemotional skills as building blocks of digital skills, which themselves are divided in several levels of complexity. The levels of digital skills may also be pyramidal: tasks requiring more complex digital skills, for example, may not explicitly use less complex ones, but the underlying knowledge and skills encompassed in basic digital skills can be seen as pillars of more complex digital skills.

## _Defining digital, cognitive, and socioemotional skills_

We define three main skills categories, each with three subsets of skills. The main categories are digital, cognitive, and socioemotional. Skills that do not fit these categories are defined as other technical and language skills.

Digital skills are the skills that are needed to work with information and communications technology (ICT) software and devices. Digital skills allow people to access and use digital technologies. Digital skills are often mistaken for a narrow set of specialized skills used by ICT workers, such as programming or data science. However, digital skills encompass a wide range of complexity and are increasingly used across many kinds of tasks in a wide range of occupations (UNESCO 2017, IFC 2019, ITU 2020, DE4A 2021). Figure 1 presents the decision tree that we use to identify and classify digital skills.

> 3 Cognitive and socioemotional skills are themselves complementary to each other. In the U.S., job requirements for cognitive and social skills are positively correlated with pay and firm performance, and more so when both skills are required (Deming and Kahn 2018). Longitudinal data of adults, also for the U.S., show that wage returns to cognitive and socioemotional skills are increasing (Weinberger 2014, Deming 2017). Online job postings collected in Ukraine show that the shares of postings requiring cognitive and socioemotional skills within an occupation are very similar (Muller and Safir 2019).

5
<!-- Start of picture text --> Is the skill about a software?<br>=a<br>Is a computer or similar Is it classified as "Information and<br>device required for this skill? Technology" by Burning Glass?<br>| No | Most likely || cs<br>a TM singe anni<br>Is ita ; : : -<br>pase: Issoftwareit a basicor Issoftwareit a basic or Is it: A<br>appliedes the skill refer to knowledge ahs media for media for | |SPecelzed|<br> with software and device or a ie a 5 general use? general use? ee<br>software function? . SL J } .<br>Knowledge<br>applied with Software<br>software and function<br>device<br>Is the knowledge about IT?<br>fy an4 ‘4<br>a :<br>Non-digital f , | Intermediate | foam rEs sa<br>skills No \ digital skills } ae digital skills }<br>i | 7 \,bh A, \ .<br>hn<br>Implicit digital<br>skills, not<br>classified as<br>digital skills<br>\ Advanced digital<br>skills<br><!-- End of picture text -->
workers use existing technologies for analysis, creation, management, and design, for example engineers using modeling software and architects using design software. The advanced level refers to specialized ICT skills such as software design, managing cybersecurity, and data science using big data analytics and artificial intelligence.

**Table 1. Definitions of digital skills levels**

|**Level**|**Definition**|**Examples of tasks**|
|---|---|---|
|Basic|Ability to access and use<br>digital technologies to<br>perform basic tasks|1. Functional use of digital devices<br>2. Online communication via emails<br>3. Using software for presentations, basic spreadsheet use<br>4. Finding, managing and storing digital information and content (e.g. social media)|
|Intermediate|Ability to use professional<br>software for analysis,<br>creation, management, and<br>design|1. Using professional software for analytics, accounting, project management<br>2. Digital marketing, social media analytics<br>3. Web design, graphic design|
|Advanced|Ability to perform<br>specialized ICT tasks|1. Computer programming<br>2. Cloud computing, network management<br>3. Artificial intelligence<br>4. Data science, big data analytics<br>5. Cyber security<br>6. Web development, search engine optimization|

_Source_ : adapted from UNESCO (2017) and IFC (2019).

Cognitive and socioemotional skills are commonly identified as two fundamental broad skills categories that people use in their work and life. Cognitive skills are our mental abilities to think, learn, and solve problems (Almlund et al. 2011). They range from basic academic knowledge (literacy and numeracy) to more demanding tasks, such as critical thinking, problem-solving, and time management. Socioemotional skills are the attitudes and behaviors upon which we manage personal and social situations (Guerra, Modecki, and Cunningham 2014; Weissberg et al. 2015). Technical skills are commonly identified as a separate skillset, though they may also be thought of as a subset of cognitive and socioemotional skills that are the know-how to carry out one’s specific job, which includes digital skills. These three broad categories of skills are used on the job (World Bank 2018).

We identify three subsets of cognitive skills and three subsets of socioemotional skills. Using lists of skills that employers commonly demand identified in previous work, we select three cognitive subsets: communication, organization, and thinking, which summarize 21 individual skills (Table 2) (Cunningham and Villaseñor 2016; Muller and Safir 2019). We also define three socioemotional 7
subsets: emotions, relationships, and personal growth, which summarize 25 socioemotional skills (Table 3).<sup>4</sup>

**Table 2. Subsets of cognitive skills**

|**Subset skill category**||**Skills**|
|---|---|---|
||1.|Analytical Skills|
||2.|Creative Problem Solving|
|Thinking|3.<br>4.|Critical Thinking<br>Decision Making|
||5.|Independent Thinking|
||6.|Problem Solving|
||7.|Communication Skills|
||8.|Oral Communication|
|Citi|9.|Presentation Skills|
|ommuncaon|10.|Public Speaking|
||11.|Verbal / Oral Communication|
||12.|Written Communication|
||13.|Goal Setting|
||14.|Meeting Deadlines|
||15.|Multi-Tasking|
||16.|Organizational Skills|
|Organization|17.|Planning|
||18.|Prioritizing Tasks|
||19.|Scheduling|
||20.|Strategic Planning|
||21.|Time Management|

**Table 3. Subsets of socioemotional skills**

|**Subset skill category**||**Skills**|
|---|---|---|
||1.|Coping Strategy|
|Emotions|2.|Detail-Orientated|
||3.|Due Diligence|
||4.|Articulate|
||5.|Building Effective Relationships|
||6.|Conflict Management|
||7.|Cultural Awareness|
||8.|Listening|
||9.|Mentoring|
||10.|Negotiation Skills|
||11.|People Management|
|Relationships|12.|Persuasion|
||13.|Staff Management|
||14.|Supervisory Skills|
||15.|Teaching|
||16.|Team Building|
||17.|Team Management|
||18.|Teamwork / Collaboration|
||19.|Leadership|
||20.|Thought Leadership|
||21.|Creativity|
||22.|Initiative|
|Personal growth|23.|Positive Disposition|
||24.|Self-Motivation|
||25.|Self-Starter|

4 Although we base our classification on previous work (Cunningham and Villaseñor 2016; Muller and Safir 2019), we acknowledge that alternative categorizations are possible.

8
# **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.

9
<!-- Start of picture text --> Vacancies Malaysia 2016-18 Cambodia 2020 Malaysia 2017 Thailand 2017 m Vietnam 2017<br>40<br>35<br>30<br>g<br>E<br>2S5 os<br>Ss<br>3<br>2§ss 20<br>‘S<br>215<br>5<br>2<br>om<br>40<br>5<br>o Managers Professionals Technicians and Clerical support Service andsales Skilled agricultural, Craft andrelated Plant and machine Elementary<br>associate workers workers forestry andfishery trades workers operators, and occupations<br>professionals workers assemblers<br>High-skilled occupations Medium-skilled occupations Low-skilled<br>occupations<br><!-- End of picture text -->
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.

11
<!-- Start of picture text --> Frequency<br>oO 50,000 100,000 150,000 200,000 250,000<br>English<br>Communication Skills<br>Chinese<br>Teamwork / Collaboration<br>Sales<br>Planning<br>Microsoft Office<br>Microsoft Excel<br>Problem Solving<br>Accounting<br>Customer Service<br>Budgeting<br>Computer Literacy<br>Creativity<br>Building Effective Relationships<br>Troubleshooting<br>Sales Goals<br>Organisational Skills<br>Multi-Tasking<br>Administrative Support<br><!-- End of picture text -->

|Frequency<br><br><br><br><br><br>|
|---|
|oO<br>50,000<br>100,000<br>150,000<br>200,000<br>250,000|
|English<br>Communication Skills|
|Chinese|
|Teamwork / Collaboration|
|Sales|
|Planning|
|Microsoft Office|
|Microsoft Excel<br>Cognitive<br>Problem Solving<br>Socioemotional<br>Accounting<br>HB sesic digital skills<br>CustomerService<br>ormersels|
|Budgeting<br>Computer Literacy<br>Creativity|
|Building Effective Relationships<br>Troubleshooting<br>Sales Goals<br>Organisational Skills<br>Multi-Tasking<br>AdministrativeSupport|

<!-- Start of picture text --> Cognitive<br>Socioemotional<br>HB sesic digital skills<br><!-- End of picture text -->
<!-- Start of picture text --> Frequency<br>0 5,000 10,000 15,000 20,000 25,000<br>sal<br>Java<br>JavaScript°<br>Oracle<br>Software Development<br>Software Engineering<br>Microsoft C#<br>.NET<br>LINUX<br>Hypertext Preprocessor (PHP)<br>Mysal<br>Active Server Pages (ASP)<br>Web Site Design<br>cet<br>Teradata DBA<br>HTMLS.<br>JQuery<br>systemsy'stems DevelDevelopmentpr titeLife oyeley Cycle (601<)<br>eae<br>Web Development<br><!-- End of picture text --> <!-- Start of picture text --> 0 10,000 20,000 30,000 Frequency40,000 50,000 60,000 70,000 980,000<br>MicrosoftMicrosoftOfficeExcel<br>Computer Literacy<br>MicrosoftMicrosoftPowerpoint<br>Social Word<br>Data Media Entry<br>Multimedia<br>Facebook<br>Microsoft Windows<br>Spreadsheets<br>Typing<br>Instagram<br>Microsoft Outlook<br>Youtube<br>Word Processing<br>Document Management<br>Basic Linkedin<br>Prepare InternetSpreadsheetsSkills<br>Microsoft Office 365.<br>Quicktime<br>Boolean Search<br>Microsoft Edge<br><!-- End of picture text --> <!-- Start of picture text --> Frequency<br>oO 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000<br>Teamwork/ Collaboration<br>Creativity<br>Building EffectiveDetail-OrientatedRelationships<br>Leadership<br>PositiveSupervisory Dispositionsory SkilSki<br>Negotiation Skills<br>People ManagementSelf-Starter<br>MentoringArticulate.5<br>Staff Management Teachinge<br>Listening<br>Persuasion<br>Conflict‘TeamSelf-Motivation ManagementManagement<br>TeamtativeBuildi<br>Due Diligence<br>Thought Leadershipigs<br>Cultural Awareness<br>Coping Strategy<br><!-- End of picture text --> <!-- Start of picture text --> Frequency<br>fe) 5,000 10,000 15,000 20,000 25,000<br>SAP<br>TechnicalAutoCADSupport<br>Adobe Photoshop<br>Enterprise Resource Planning (ERP)<br>Graphic Design<br>Adobe Illustrator<br>E-Commerce<br>Accounting Software<br>Digital Marketing<br>Microsoft Project<br>Adobe Indesign<br>Troubleshooting Technical Issues<br>Information Systems<br>It Support<br>Network Ape lication SupportPpa<br>Hardware Hardware/Softwareand SoftwareMaintenanceInstallation<br>Computer Numerical Control (CNC)<br>Online Marketing<br><!-- End of picture text --> <!-- Start of picture text --> Frequency<br>ie) 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000<br>CommunicationProblemPlanningSolvingSkills<br>Organisational Skills<br>Multi-Taskingan<br>Meeting Deadlines<br>Alnalyticali tical SkillsSkill:<br>Presentation Skills<br>Time Management<br>Written Communication<br>Scheduling<br>Verbal / Oral Communication<br>Decision Making<br>Quick Learner<br>Strategic Planning<br>Oral Communication<br>Critical Thinking<br>Creative Problem Solving<br>strategicPublic SpeakingThinking<br>Prioritising Tasks<br>Goal Seti<br>independ en ne<br>jependentBasic MathematicsThin ne<br><!-- End of picture text --> <!-- Start of picture text --> Frequency<br>©———————————————————— 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000<br>ls eee<br>AuTi Eaa a a<br>CustomerBudgetin Service Pe|a |<br>ko.i |a a<br>Ws eee——— ass<br>AdministrativeBusiness DevelopmentSuppot a [=es<br>. Withae aaaa——Eeeeeeeee)rs<br>Project ManagementRserch aaa<br>Marketinga [ey<br>File Management [ey<br>Quality Assurance and. Control. (exeetety[|<br>Prospective Clients, ===|<br>Business AdministrationCost Control aoo<br>{|__|<br>Customer ContactFinance SEE<br>——<br><!-- End of picture text -->
# _Measuring skills requirements by occupation_

We calculate two measures of skills requirements. The first measure is the skills count, that is the average number of times a skill from a given skill category appears in job postings mapped to a given occupation. We use this measure to explore complementarities among the nine subsets of skills categories (advanced, intermediate, and basic digital skills; three subsets each of cognitive and socioemotional skills and their subsets) within occupations.

Our second skills measure is the average relative frequency with which a skill category appears in an occupation. The measure is calculated by averaging for each occupation the number of times a skill category appears in the job posting mapped to that occupation divided by the total count of skills in the job postings for that particular occupation (that is, expressed as a share of total skills rather than the raw skill count).

# **3.2. Creating occupational skills profiles and digital occupation levels**

# _Using the online job postings data to create occupational skills profiles_

We take two additional steps to create our final data set. First, we map each job posting classified with the 2013 Malaysia Standard Classification of Occupations (MASCO) and its related skills profile to the 2008 International Standard Classification of Occupations (ISCO-08; see ILO 2012), the international occupational classification system.<sup>15</sup> This gives us occupational skill profiles for the occupational codes in the employment data from other countries.

Second, we impute the occupational skills profiles of 27 occupations. Among the 127 3-digit ISCO-08 occupations, the online job postings data did not include job postings for 16 occupations — primarily agricultural workers and street vendors — and 11 did not have sufficient job vacancy postings to generate a skills profile for that occupation.<sup>16</sup> These 27 occupations roughly represent between 20 and 40 percent of employment in the four countries studied.<sup>17</sup> Omitting them would distort the distribution of digital skills in a country. Instead, we impute their skills profiles by identifying similar

> 15 Job vacancies are mapped to ISCO codes at the 4-digit level.

> 16 We defined a threshold of at least 45 job postings per occupation.

> 17 The imputed occupations with employment shares exceeding 1 percent in any of the four countries (and the ISCO code) in the sample are: street and market salesperson (521), market gardeners and crop growers (611), animal producers (612), fisheries workers/hunters/trappers (622), subsistence crop farmers (631), subsistence livestock farmers (632), subsistence fishers/hunters/trappers/gatherers (634), street vendors (excluding food) (952). The full list of imputed occupations, and the occupations used to impute their skills profiles, are provided in Annex 1.

14
occupations in the job postings data and use the skills profiles of those occupations as a proxy for the skills profile of missing occupations.<sup>18</sup>

# _Description of the occupational skills profiles_

The average number of, and types of, skills listed in (or imputed from) the job vacancy data differs by occupation. Occupations require an average of 6 skills, ranging from 2.2 to 12 skills (Table 4). Heavy truck and bus drivers require the fewest skills (2.2) while software and applications developers and analysts are at the other end of the distribution requiring 12 defined skills. On average, occupations require one digital skill, less than one cognitive and socioemotional skill each, and more than three other skills, which include languages and other technical skills, highlighting the high degree of jobspecific skills.

**Table 4. Count and share of requirements for skills categories and their subsets across the 127 occupations**

||**Variable**|**Mean**|**Std dev**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|**Average number of skills**|**Total**|5.6|1.8|2.2|12.0|
|**(count)**|**Digital**|1.0|1.1|0.0|7.6|
||Advanced|0.3|0.8|0.0|6.4|
||Intermediate|0.3|0.4|0.0|2.5|
||Basic|0.4|0.2|0.0|1.9|
||**Cognitive**|0.7|0.3|0.2|1.3|
||Thinking|0.1|0.1|0.0|0.3|
||Communication|0.3|0.1|0.1|0.6|
||Organization|0.3|0.1|0.1|0.6|
||**Socioemotional**|0.5|0.2|0.1|1.2|
||Emotions|0.0|0.0|0.0|0.2|
||Relationships|0.3|0.2|0.1|1.0|
||Personalgrowth|0.1|0.1|0.0|0.6|
||**Others**|3.4|0.9|1.5|6.5|
|**Average share among total**|**Digital**|12.7|10.6|1.0|58.1|
|**skills**|Advanced|3.2|6.4|0.0|47.5|
||Intermediate|3.9|4.8|0.0|28.5|
||Basic|5.6|5.2|0.3|51.0|
||**Cognitive**|10.9|2.9|3.6|18.5|
||Thinking|1.2|0.7|0.1|3.9|
||Communication|5.3|1.9|1.0|10.3|
||Organization|4.2|1.5|1.2|9.2|
||**Socioemotional**|8.3|5.0|2.1|42.2|
||Emotions|0.5|0.5|0.0|4.3|
||Relationships|5.8|4.5|1.3|38.2|
||Personal growth|2.0|1.5|0.3|8.6|
||**Others**|68.2|10.6|28.6|84.3|

> 18 We use two sources to define similar occupations. ILO (2012) lists related occupations in ISCO-08. The O*NET Career Changers Matrix lists occupations in the US that have similar skills and experience and that workers could transfer between with minimal additional preparation (O*NET 2021). We only use these two sources to identify potential similar occupations. Once identified, the imputed occupations take the average share of skills requirements for each skills category of their similar occupations in the Malaysia data (or the skill profile of the similar occupation when the similar occupation is only one).

15
_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.

16
compared with the other digital skills (e.g., intermediate and basic). These variables will inherently discriminate between occupations having the same total share of digital skills, but their respective digital skill components are different.<sup>21</sup> We include six variables, each a ratio of advanced, intermediate, basic or a combination thereof.<sup>22</sup> The final two variables are rank variables. We expect that adding the variables corresponding to the “rank of the occupations with respect to the share of advanced digital skills” further reinforces the signal that we want the model to learn. Using the rank variables discourages the model from grouping together occupations that belong in opposing regimes of the share of advanced digital skills. This rationale holds true for the second variable, namely the “rank of the occupations with respect to the total share of digital skills.” We apply K-means clustering using the above 13 variables. The resulting five clusters of occupations can be ranked in their degree of digitalization by averaging the values of the “share of total digital skills” and “share of advanced digital skills” (Figure 5.A). We then analyze the resulting clusters to assess the quality of the groupings. Our analysis suggests that two of the five clusters generally share similar characteristics. To simplify the presentation and given the similarity of their digital skills, we combine the second and third clusters (numbers 2 and 3 in Figure 5.A) into a single cluster, resulting in four digital skills levels (Figure 5.B).<sup>23</sup> The full list of occupations assigned to each of the four levels, and the share of skills in each occupation that are advanced, intermediate, and basic digital skills, is provided in Annex 2.

> 21 For example, two occupations both have 50% total share of digital skills but the first has (40% Advanced, 5% Intermediate, 5% Basic) digital skills while the other has (5% Advanced, 5% Intermediate, 40% Basic) digital skills.

> 22 The variables are: (1) ratio of Advanced digital skills to the total share of digital skills, (2) ratio of Intermediate digital skills to the total share of digital skills, (3) ratio of Basic digital skills to the total share of digital skills, (4) ratio of the sum Advanced and Intermediate digital skills share to the total digital skills share, (5) ratio of the sum Advanced and Basic digital skills share to the total digital skills share, and (6) ratio of the sum Intermediate and Basic digital skills share to the total digital skills share.

> 23 The empirical results when using the four-cluster classification are similar to those when using the five-cluster classifications (Annex 7).

17
<!-- Start of picture text --> x5 ad 03 Oo? #1<br>50<br>x<br>45<br>x<br>= 40<br>= 35<br>—<br>ww<br>we 30<br>a<br>ao<br>2 25 : -<br>= x<br>@ 20<br>S15<br>vx<br>a&10 x x<br>5 sat * un x<br>5 Le OF eaeee ty n° 4 4 x x<br>0 10 20 30 AQ 50 60<br>Share oftotal digital skills (%)<br><!-- End of picture text --> <!-- Start of picture text --> 4 High digital occupations<br>@ Medium digital occupations<br>® Low digital occupations<br>¢ Very low digital occupations<br>50<br>A<br>45<br>= A<br>= 40<br>=<br>a)<br>8<br>30<br>i<br>a ri A<br>= 25<br>= 20 “<br>a<br>e 1s<br>co A<br>“10<br>A 4<br>5 on. & # 4 °<br>0 10 20 30 40 50 60<br>Share of total digital skills (%)<br><!-- End of picture text -->
The “high digital” occupational cluster includes 13 occupations (Table 5). All require all levels of digital skills. All also have a high share of digital skills among total skills, ranging from 28 to 58 percent. The high-digital cluster includes, for example, software developers, a high-skilled occupation, and keyboard operators, a medium-skilled occupation. Both have a similar share of their skills that is digital (56-58 percent) but software developers mostly require advanced digital skills while keyboard operators mostly require basic digital skills. The bulk of these 13 occupations are consistent with the ISCO taxonomy of information technology occupations and most of these 13 occupations also have high scores on an index of digitalization created for occupations in the United States based on O*NET data (Muro et al. 2017).<sup>24,25</sup>

**Table 5. Digital skill and number of occupations of digital levels based on clusters**

|||**Av**|**erage share **|**of digital skills (**|**%)**||
|---|---|---|---|---|---|---|
||||||||
||||||||
|**user**<br>**number**|**ga**<br>**level**|**Total**|**Advanced**|**Intermediate**|**Basic**|**umer o**<br>**occupations**|
|0|Very Low|4.8|0.8|1.0|2.9|38|
|1|L|8.1|2.1|1.7|4.2|20|
|2|ow|11.5|1.1|3.5|7.0|21|
|3|Medium|15.0|3.0|5.0|7.1|35|
|4|High|38.3|15.8|13.3|9.3|13|

_Source:_ Authors’ calculations based on World Bank-Burning Glass online Malaysia job postings data (2016-18). _Notes_ : Darker red colors show higher percentage.

Thirty-five occupations map to the “medium digital” occupation cluster. These occupations have low requirements for advanced digital skills, but a moderate demand for intermediate- and basic-digital skills. This category includes machine operators, technical education teachers, and office clerks, who regularly use specialized software to carry out their jobs.

Most occupations can be classified as low digital. To provide some nuance to this group, we maintain the two clusters that emerged from the exercise and name them “low digital” and “very low digital” occupations. The exercise maps 41 occupations to the low-digital occupation category. These occupations require a moderate use of basic-digital skills. A broad range of occupations are in this category, including transport specialists (like Grab drivers who use digital phone apps to identify their next transport gig job), chief executives who may use MS Office for daily work, or salespeople who operate cash registers and inventory control software. The very low digital jobs include 38

> 24 Eight of the thirteen occupations are information technology occupations as defined by the ISCO occupation classification (ILO 2012, pp. 25-26). The two occupations we define as high digital that are not ICT occupations are mathematicians, actuaries, and statisticians (ISCO code 212) and engineering professionals (excluding electrotechnology) (ISCO code 214). 25 The occupation digital score is based on occupations’ three digitalization aspects as measured by O*Net for occupations in the US —computer & electronics, programming, and interacting with computer— following the methodology by Muro et al. (2017).

19
occupations. These occupations require few digital skills, and the digital skills that they do require are mostly basic digital skills. Cooks, who might use computers or smartphone for purchasing or inventory management, and Early Child Development teachers, who may use digital technologies to access or provide lessons, are examples of occupations in the very low digital occupations category.<sup>26</sup>

## **3.3. Matching the occupational skills profiles with country employment data: The Southeast Asia Digital (SEAD) data set**

To analyze the skills profile of the employed population in the four countries we study, we create the Southeast Asia Digital (SEAD) data set that matches the occupation skills profiles to country employment data. We use the occupation variable to do the match with data from four Southeast Asian countries: the 2020 Cambodia Socioeconomic Survey and the 2017 labor force surveys in Malaysia, Thailand, and Vietnam. Each country data set is representative of the population at the national level, allowing us to quantify the number of people working in an occupation. As such, we have a data set of occupation skills profiles for 127 occupations and weights representing the occupations’ employment share in the four countries.

# **4. Methodology**

To explore the complementarity of digital and other skills and the levels of occupation digitalization, we use descriptive statistics, pairwise correlations, a factor analysis, and linear probability model (LPM) regressions.

## _Similarity of skills required in very low-, low-, medium-, and highly digital occupations_

After an inspection of summary statistics, we apply an LPM to estimate the probability that a skill _i_ in occupation _o_ is found in a particular digital occupation group _g_ . We use an LPM to estimate marginal effects of binary outcomes following Friedman (2012) and Bellemare (2015, 2018). The LPM is as follows: where _yog_ is a binary variable representing occupation _o_ dependent on the (digital occupation level) groups _g_ of interest (e.g. medium- vs. low-digital occupations, high- vs. low-digital occupations, etc.). _Doi_ is a dummy variable for skill _i_ of occupation _o_ taking the value of one if the skills count of a skills category of an occupation is above the mean. β _oig_ is the coefficient for the groupings _g_ quantifying the

> 26 An alternative methodology that uses a smaller set of variables to assign occupations to high, medium, and low digital categories resulted in a similar mapping. Results are available from the authors.

20
effect of the dummy variable for skill _i_ in occupation _o_ . α is a constant, and ε is the error term. The dummy variable for a given skill _i_ of occupation _o_ is derived as follows:

> o 0, o � where _xoi_ represents the average count of skill _i_ in occupation _o_ and � is the mean of _xoi_ over all occupations, where _i_ is a vector of six skills subsets. Three of the skills subsets are cognitive skills: ıı xx thinking, communication, and organization. The other three are socioemotional skills: emotions, relationships, and personal growth. The dummy variable is used for easier interpretation of the LPM’s coefficients than the raw skills count variables would allow. This transformation, in the context of LPM, allows us to interpret the coefficients as percentage point increase (or decrease) towards the probability of being a given occupation digital level if the raw skills count for the variable is above (or below) the mean of the skills count over all occupations.<sup>27</sup>

We run the above analysis to be representative of the employed population of Malaysia (around 14 million of workers). Since we are interested in understanding the skills required by employers in a country-specific job market, we use the Malaysia subsample of the SEAD, with each occupation being weighted by the percentage of the employed population in it.<sup>28</sup> We carry out two additional analyses using different subsamples. First, we run the LPM with an unweighted sample to understand the correlation between skills within occupation, irrespective of the occupational composition of the workforce. Second, we carry out a similar analysis using the Cambodia, Thailand, and Vietnam subsamples. The results are similar to those from the Malaysia subsample so, in the interest of parsimoniousness, mostly present the results from the Malaysia subsample.<sup>29</sup> Results for the occupation skills profile (unweighted by the distribution of occupations in a particular country) and replications for other countries are presented in Annex 3.

> 27 Since the outcome is binary and we are using a probability linear model, we can interpret the coefficient of a dummy variable to be a percentage point contribution towards the probability of the outcome. For illustration, assume a PLM model with a constant value of zero. If we analyze one non-zero dummy variable and let all the other variables take a value of zero, then the coefficient of the non-zero dummy variable will equate to the probability of the outcome. Adding another non-zero dummy variable will linearly increase or decrease the probability according to the value of the coefficient. A regression using the raw skills count is available from the authors.

> 28 Studies on job skills demand and technological similarly combine data on job tasks at the occupational level from the O*NET database and individual-level employment survey that weight each occupation by the percentage of the employed population in each occupation (Autor, Levy, and Murnane 2003; Hardy, Keister, and Lewandowski 2018; Caunedo, Keller, and Shin 2021).

> 29 By construction, the variation between the results with occupational skills profile and those for the employed populations are only due to differences in the distribution of the employed population across occupations.

21
# _Complementarities between digital and non-digital (cognitive, and socioemotional) skills_

Next, we investigate complementarities between digital, cognitive, and socioemotional skills in Malaysia’s jobs.<sup>30</sup> We first examine pairwise correlations. The pairwise correlations show the magnitude of relationships between two subsets of skills. We use Pearson correlations and test the significance of the correlation through t-test.

We then carry out a factor analysis using the skills count of the nine digital, cognitive, and socioemotional skills subsets (three each) detailed in section 2. The exploratory factor analysis allows us to model the relationships among skills subsets by reducing them to fewer variables thus highlighting stronger links between several skills subsets. We use a standard exploratory factor analysis that finds a number q of factors that linearly reconstruct our nine original variables according to the following relationship: where is the value of the _i_ th observation on the _j_ th variable, is the _i_ th observation on the _k_ th common factor, yyooii i is the set of linear coefficients called the factor loadings, and zzooii is similar to a residual but is known as the _j_ th variable’s unique factor (StataCorps 2019). We use varimax rotation ooii bb ee to produce orthogonal factors,<sup>31</sup> to get maximized factor loadings. Again, we use the SEAD data for Malaysia, weighted by the distribution of employment across occupations, to study those relationships within the existing job structure. Occupation-level results (unweighted by the distribution of occupations in a particular country) and replications for other countries are also estimated and presented in Annex 4.

# _Occupation digitalization and skills requirements in Cambodia, Malaysia, Thailand, and Vietnam_

Finally, we use descriptive statistics to calculate the levels of occupation digitalization and skills requirements across the four countries we study. We use the SEAD data set to understand the distribution of skills across the four countries of interest. We also generate occupation-weighted data sets over several years for Malaysia, Thailand, and Vietnam to understand how the distribution of skills across each country evolves over a 4-6 year period.<sup>32</sup>

> 30 We repeat this analysis for unweighted occupations, and weighting for the Cambodian, Thai, and Vietnamese occupational distributions. Again, results are similar to those when using the Malaysia-weighted data.

> 31 By definition, orthogonal factors are not correlated with each other.

> 32 We were unable to access multiple years of the Cambodian SES.

22
# **5. The Skills Profile of Very Low-, Low-, Medium-, and High Digital Occupations: The Case of Malaysia**

The level of the digital occupation is positively correlated with the share of digital skills required by employers in Malaysia.<sup>33</sup> Using simple summary statistics, we find that one-third of the skills required in Malaysia’s highly digital occupations are digital skills, while 16 percent of the skills required in Malaysia’s medium-digital occupations can be classified as digital skills (Figure 6, panel A). An even lower share of digital skills is required in low- and very-low-digital occupations (9 and 5 percent, respectively) in Malaysia. These trends are observed in the other three sample countries, with slight differences in point estimates (Annex Table A.3.1). First, Thailand’s highly digital occupations require a higher share of digital skills (37.1) than Malaysian highly digital occupations (33.3 percent). Second, Malaysia’s low digital occupations require the fewest digital skills among the four countries (9.4 percent) compared to 9.7 percent (Vietnam), 9.8 percent (Cambodia), and 10.1 percent (Malaysia). Highly digital occupations require the most digital skills across the three skill levels. The relative importance of advanced digital skills in Malaysia is much higher for highly digital occupations: 13 percent of their required skills are advanced digital as compared to 1-3 percent for very-low, low, and medium digital occupations (Figure 6, panel A). A similar trend and point estimates are observed in the other countries, suggesting robust proportionality in advanced digital skills across digital occupation categories.

However, highly digital occupations also require a larger share of intermediate digital skills than medium digital occupations require (14 vs. 3.8 percent).<sup>34</sup> That might be because medium-digital occupations, while requiring some digital skills, mostly require skills in a job specialty (e.g. finance or marketing). This trend emerges in the other countries (Annex Table A.3.1). Similarly, the share of basic digital skills is less demanded for very-low and low-digital occupations than medium digital occupations in Malaysia (Figure 6, panel A).<sup>35</sup> This reflects that while basic digital skills are a required in those occupations, they mostly require other skills.

> 33 These results deviate from those in Table 5, which presents the average share of digital skills – total and by level – for each of the four digital occupation levels, due to the weighting of occupations by country. Table 5 assigns an equal weight to each occupation with the country-specific estimates use country weights. Thus, occupations with certain skills mixes, even within digital occupation level, may play a more prominent role in the country averages than in the unweighted average.

> 34 Cambodia is the only country where intermediate digital skills play a larger role than advanced digital skills in highly digital jobs. Annex Table A.3.1 shows that 17.5 percent of skills in Cambodia’s highly digital occupations are classified as intermediate digital skills, as compared to 5.2 percent in medium occupations. At the same time, only 8 percent of advanced digital skills are required in Cambodian highly digital occupations. Thus, Cambodia’s 13 highly digital occupations require more intermediate, rather than advanced, digital skills.

> 35 Basic digital skills are also more demanded in highly digital occupations in Cambodia, Vietnam, and especially Thailand (8.8 percent).

23
<!-- Start of picture text --> 100<br>—+e 3.1 5.0 1.62.9 Xe) 0.811<br>90 9.0 10.5<br>06 58<br>2s& 6050 a ee Po Pp BM Intermediate Advancedrn digitaldigital<br>2wv G Cognitiveses<br>Z2 40 | | 68.1 ee ALD ee enti8 ;<br>S 72.5 O Socioemotional<br>=vo 30 O Other<br>aov0 52.8<br>=<e£o=> 20<br>(e)<br>Highly digital occupations Medium digital occupations Low digital occupations Very low digital occupations<br><!-- End of picture text -->

<!-- Start of picture text --> QHigh digital occupations<br>@ Medium digital occupations<br>Low digital occupations<br>Very low digital occupations<br>35<br>Fe<br>S 30<br>oy<S<br>— 25<br>2£>S=zs 20<br>¥ 15<br>xo)<br>gz<br>=aFA&5© 105<br>z<br>0<br>Total Advanced digital Intermediate Basic digital Total Thinking Communication Organization Total Emotions Relationships Personal growth<br>digital<br>Digital Cognitive Socioemotional<br><!-- End of picture text -->
very-low-digital occupations than in highly digital occupations (Figure 6, panel B). These trends also emerge for the other three sample countries with a few exceptions (Annex Table A.3.1).<sup>36</sup> The similar requirement for cognitive and socioemotional skills across digital occupation levels in Malaysia is confirmed when controlling for potential correlations between variables. The regression analysis finds that the required subsets of cognitive and socioemotional skills are quite similar across very-low, low-, medium-, and highly digital occupations.<sup>37</sup> The most notable exception is that, on average, occupations that require more thinking skills (a subset of cognitive skills) are 209 percentage points more likely to be a highly digital occupation relative to a very low-digital occupation (Table 6). This correlation was strongly identified for the other three countries, as well. A weak positive correlation is also found between thinking skills and low (versus very low) and high (versus low) digital occupations in all countries except Thailand (Annex Table A.3.2). While more emotional skills (a subset of socioemotional skills) are expected from medium-digital occupations compared to low digital occupations in Malaysia, the size and imprecision of the estimates for the other occupation types do not reveal more general patterns. However, this relationship emerged with more precise estimates for the other three countries. Relationship skills are weakly negatively correlated with low (versus very low) and high (versus low) digital occupations, though correlations are stronger for Cambodia. A few other weak correlations emerge, but most subsets are not statistically different across occupation levels at the 5 percent significance level or below, meaning that there is no discernable difference in demand for those skills across digital occupation levels.<sup>38</sup>

> 36 Namely, relationship skills are less important for very low digital occupations in the other three countries, as compared to Malaysia, while personal growth is more prevalent among highly digital occupations.

> 37 These associations should not be interpreted as causal but rather as conditional correlations for at least two reasons. First, there are factors beyond cognitive and socioemotional skills that could influence the digital occupation level. Second, subsets of cognitive and socioemotional skills may be correlated with each other to some degrees, especially those in the cognitive subsets, and thus may bias the conditional correlations with digital occupation levels (see Table 7).

> 38 Similar results emerge from LPM estimates using the unweighted occupational skills profile sample and the countryspecific weighted occupations for Vietnam and Thailand. The communications, relationships, and personal growth variables play a greater explanatory role in the Cambodia -and, to some extent, the Vietnam - estimates, with the coefficient signs and magnitudes were similar to the Malaysia-weighted sample (Annex Table A.3.2).

25
## **Table 6. Conditional correlations between digital occupation level and cognitive and socioemotional skills requirements in Malaysia**

_LPM regressions of digital occupation levels on dummies taking the value of one if the skills count of a skills category of an occupation is above the mean_

|||Low vs.|Occup<br>|ation digita<br>|l level<br>|High vs.|
|---|---|---|---|---|---|---|
|||<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|<br>Very<br>low|
|||(1)|(2)|(3)|(4)|(5)|
|Cognitive|Thinking|**0.36***|0.22|0.01|**0.53***|**2.09*****|
|||(0.17)|(0.13)|(0.15)|(0.23)|(0.40)|
||Communication|**0.47***|-0.19|0.01|-0.17|-0.09|
|||(0.21)|(0.14)|(0.33)|(0.24)|(0.16)|
||Organization|0.00|-0.16|0.12|-0.13|-0.60|
|||(0.16)|(0.15)|(0.10)|(0.14)|(0.41)|
|Socioemotional|Emotions|-0.00|**0.48****|-0.30|0.12|0.81|
|||(0.15)|(0.15)|(0.21)|(0.19)|(0.41)|
||Relationships|**-0.30***|-0.35|0.08|**-0.58***|-0.67|
|||(0.14)|(0.18)|(0.38)|(0.24)|(0.43)|
||Personal growth|-0.16|-0.03|**0.36***|0.34|-0.55|
|||(0.16)|(0.17)|(0.18)|(0.20)|(0.41)|
||Constant|0.44**|1.47***|2.22***|1.39***|0.74|
|||(0.14)|(0.14)|(0.22)|(0.33)|(0.54)|
||N|79|75|47|54|51|
||R-sq|0.36|0.36|0.13|0.15|0.44|

_Source_ : Authors’ calculations based SEAD data.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224).

_*p < 0.1 **p < 0.05 ***p < 0.01_

# **6. Complementarities among Digital, Cognitive, and Socioemotional Skills: The Case of Malaysia**

Cognitive and socioemotional skills correlate more strongly with basic digital skills than with intermediate and advanced digital skills in Malaysia. Basic digital skills correlate highly with all three subsets of cognitive skills and with one socioemotional skill in Malaysia. The correlation coefficient between basic digital skills and thinking, communication, organization (cognitive), and emotions (socioemotional) is around 0.5 (Table 7). Basic digital skills are not correlated with relationships or personal growth in Malaysia. In contrast, intermediate digital skills correlate with thinking and 26
<!-- Start of picture text --> Digital Cognitive Socioemotional<br>Advanced Intermediate Basic Thinking Communication Organization} Emotions Relationships Personal growth<br>Digital Intermediate 0.46***<br>Cognitive Thinking 0.46*** 0.42*** 0.45***<br>Communication 0.17 0.15 0.51***<br>Organization 0.11 0.30***<br>Socioemotional § Emotions 0.15 0.28** 0.45*** 0.50***<br>Relationships 0.28** 0.49*** 0.29*** 0.16<br>Personal growth 0.398" 0.18* 0.26**<br><!-- End of picture text -->
(Almlund et al. 2011) (Table 7). The three socioemotional skills, by contrast, correlate little between each other in Malaysia, except for personal growth and relationship skills that correlate around 0.3, reflecting that they mostly capture different facets of socioemotional skills. The correlations between the digital skills subsets are modest for Malaysia but significant between basic and intermediate (0.3), much higher between intermediate and advanced (0.5). The correlation is close to zero between basic and advanced digital skills in Malaysia.<sup>40</sup> This may be due to employers who wish to hire workers with advanced digital skills assume that the potential hires already have basic digital skills, so they do not mention the basic skills in the job postings. The last correlation is lower than estimated for professional occupations in the U.S. that have a correlation of 0.2 between their basic and advanced digital skills (Deming and Kahn 2018).

An alternative methodology to consider not only pairwise relationships between skills subsets but possible systematic association between more subsets shows stronger complementarities.<sup>41</sup> Exploratory factor analysis finds that three groups of skills break out across the 9 digital, cognitive, and socioemotional subsets for Malaysia. The first factor is a mix of the three cognitive skills subsets, relationship skills (socioemotional) and basic digital skills (Table 8). It appears to be a factor of less digital skills for a broad range of occupations in Malaysia. The occupations with more of this skills mix are low- and medium digital occupations, such as government professionals and plant and machine operators (Annex Table A.5.1). The second factor is based on intermediate and advanced digital skills and to a lesser extent thinking skills (cognitive) and personal growth (socioemotional).<sup>42</sup> The typical occupations in Malaysia requiring a high weight on these kinds of skills are highly digital occupations, such as software developers and network professionals. The third factor heavily loads on the socioemotional skills subsets of emotions and personal growth.<sup>43</sup> Typical Malaysian occupations using this skill set include occupations from all three levels of occupation digitalization, such as architects and primary school teachers. Similar factors emerge for the other three countries.<sup>44</sup>

> 40 The other three countries and the unweighted occupation profile data find similar results (Annex Table A.4.1).

> 41 Very similar results are found when not rotating factors and using oblique rotations (i.e. allowing for correlations across factors) (results available upon request).

> 42 The socioemotional skills that weighed most heavily in this factor in Vietnam was organization and in Thailand was relationship skills (Annex Table A.4.2).

> 43 The third factor is the least stable across the alternative subsamples. For example, in Cambodia, it only weighs heavily on advanced digital skills. In Thailand, the highest factor loadings are on personal growth and a lower loading on intermediate digital skills, though very low basic skills, as in the Malaysia-weighted sample. The Vietnam-weighted sample also weighs very heavily on personal growth and very low on all other skills (Annex Table A.4.2).

> 44 A notable exception is that an “advanced digital” factor emerges for Cambodia. No other skills weight positively heavily in this factor while socioemotional skills are particularly negative (Annex Table A.4.2).

28
<!-- Start of picture text --> Factors<br>‘Skills category Skillsubset 1 2 3 Uniqueness.<br>1 Digital Advanced 0.15 0.07 0.43<br>2 Digital Intermediate 0.20 0.07 0.20<br>3. Digital Basic 0.09 | 0.34<br>4 Cognitive Thinking 0.36 0.07 0.22<br>5 Cognitive Communication -0.02 0.32 0.23<br>6 Cognitive Organization 0.14 0.23 0.29<br>7 Socioemotional Relationships 0.16 -0.06 0.42<br>8 Socicemotional Emotions 0.34 0.22<br>9 Socioemotional Personal growth 0.4 | 0.24<br><!-- End of picture text -->

<!-- Start of picture text --> Factor Skills subset _ Skill category<br>Communication Cognitive<br>Organization Cognitive<br>Factor] Thinking Cognitive<br>Rel bi ; eosnal<br>—" ediate Digital<br>Factor 2 Advanced Digital<br>Emounns GOcinematonall<br>Factor3psonal erowth Socioemotional<br><!-- End of picture text -->
<!-- Start of picture text --> 0.8 47 2 15<br>100 ——— Cd -—— ~———<br>90<br>80<br>19.9<br>z 16.3<br>@ 70 22.3<br>E<br>a<br>5 60 DHighly digital occupations<br>s 50 30.7 @ Medium digital occupations<br>2 Low digital occupations<br>Hi“ 40 @ Very low digital_ occupations-<br>30 = 58.4 ><br>20 40.2<br>10<br>0<br>Cambodia Malaysia Thailand Vietnam<br><!-- End of picture text -->
<!-- Start of picture text --> © nnn<br>_=<br>Cc<br>25<br>3<br>a] Malaysia C)<br>8 Or smcccccnnnnnji<br>g<br>i)<br>a<br>><br>=<br>ao28 2<br>5 Thailand<br>2 Vietnam<br>m7) Cambodia O<br>0 _ ee es eeae |<br>0) 2,000 4,000 6,000 8,000 10,000 12,000<br>GDP per capita (current USS)<br><!-- End of picture text -->

<!-- Start of picture text --> Dmineceeaeaauesueeueeuesueeeeeneeereneste<br>= Malaysia<br>c<br>ao)<br>a]<br>rs}<br>ho 10<br>oO<br>3 18 hailand<br>= Vietnam<br>|<br>3 16<br>£<br>wenn<br>2 Cambodia<br>2<br>fe) 2,000 4,000 6,000 8,000 10,000 12,000<br>GDP per capita (current USS)<br><!-- End of picture text -->
<!-- Start of picture text --> O Cambodia @ Malaysia B Thailand @ Vietnam<br>V0<br>10.5<br>10.095 ||<br>2 90 es| ||<br>S35 ||)| |<br>& 80 moo<br>=gS 75 | |||| ||| |<br>Ss 5¢5 ||| ees—sesesS<br>BieS | | ||<br>8 55<br>a3. 45 | | ee | es || el |<br>| ||<br>5® 35 || |<br>a2eee)25 | | eea a| aee eS)|||) ||) ||) ae |<br>| | | | | a |) a) | a a) a) | || | | |<br>rt J oo ea ae a<br>os | I a ee |<br>0.0 ee eee ef ee<br>Total Advanced _ Intermediate Basic Total Thinking — Communication Organization Total Emotions Relationships Personal growth]<br>Digital Cognitive Socioemotional<br><!-- End of picture text -->
A short time series finds a slow transition to digital occupations in Southeast Asia, with only Vietnam showing a trend toward greater digitalization. When expanding the sample to annual labor force surveys in Malaysia (2011-17), Thailand (2014-20), and Vietnam (2014-15, 2017-18), we find that any changes in the occupational structure of labor markets in Malaysia and Thailand did not affect the distribution of the workforce across digital skills levels (Figure 10). In contrast, the data from Vietnam finds a decline in very low digital occupations (from 70 percent to 64 percent over the five-year period) and a 3-percentage point increase in low- and medium digital occupations. The micro-data explain these changes, where the share of workers in occupations related to subsistence agriculture — very low digital occupations — declined significantly over the period with slow increases in craft occupations (assemblers, construction, machine operators) and services (shop salespeople) which are classified as low or medium digital occupations.

33
<!-- Start of picture text --> 100 fea] |e =] os<br>90<br>80<br>5 70<br>—<br>a<br>= 60 DHighlydigital occupations<br>iG© 50 28.4 28.7 28.9Q 29.2 23.65 Oe. 30.SAW) @ Medium digital., occupations-<br>v @ Low digital occupations<br>GHS 40 Very low digitala occupations.<br>30<br>20 38.6 39.8 40.0 40.0 40.9 40.5 40.2<br>10<br>0<br>2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

<!-- Start of picture text --> 2.0 2.1 2.0 2.0 2.0 2.0 21<br>90<br>80<br>25= 7060 21.9 22.1 22.5 22.3 21.7 22.1 22.1 DHighly digital occupations<br>5 @ Medium digital occupations<br>ay 50<br>v @ Low digital occupations<br>GHS 40 Very low digitala occupations.<br>30 58.6 58.3 57.7 58.3 59.1 58.4 58.4<br>20<br>10<br>0<br>2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> <!-- Start of picture text -->
1.3 14 15 15<br>100 ——— —— ~—<—— —<br>90<br>80<br>14.1° 14.8<br>+ 70 16.3 16.8<br>—<br>a<br>= 60 D Highly digital occupations<br>& @ Medium digital occupations<br>ira} 50<br>v @ Low digital occupations<br>aS 40 <0 B Very low digital- occupations.<br>30 65.0 64.0<br>20<br>10<br>0<br>2014 2015 2016 2017 2018<br><!-- End of picture text -->
# **8. Conclusion**

Our study confirms that digital skills are common in Southeast Asia. The job postings data from Malaysia in 2016-18 reveals that all occupations require at least some digital skills. Highly digital occupations, however, still represent a very small share of employment in the four countries studied: 5 percent in Malaysia, around 2 percent in Thailand and Vietnam, and 1 percent in Cambodia. Instead, the bulk of employment is in very-low-digital occupations (from 40 percent in Malaysia to 66 percent in Cambodia) and low-digital occupations (from 16 percent in Vietnam to 31 percent in Malaysia). These shares are changing very slowly over time, with the exception of Vietnam’s somewhat rapid shedding of very low digital occupations in favor of low and medium digital occupations We find that occupations from all four digital occupations levels need cognitive and socioemotional skills. While the share of intermediate and advanced digital skills differs across digital occupation levels in Malaysia, all four levels of occupations require roughly the same share of basic digital skills (5-9 percent), cognitive skills (8-12 percent), and socioemotional skills (6-12 percent). Cognitive and socioemotional skills are even more important than digital skills for very-low, low-, and mediumdigital occupations (nearly the entire employed population). The share of cognitive and socioemotional skills combined is greater than digital skills: 23 versus 5 percent for very-low digital occupations, respectively, 20 versus 9 percent for low-digital occupations, respectively, and 16 versus 15 percent for medium-digital occupations, respectively. Conditional correlations estimated through regressions show that medium- and highly digital occupations require more thinking skills (cognitive) than very low digital occupations but require similar levels of the other cognitive and socioemotional subsets as do occupations that are less digitally intensive. Similar patterns emerge for Vietnam, Cambodia, Thailand.

When disaggregating cognitive and socioemotional skills, subsets of these skills categories are found to associate more closely with certain levels of digital skills. Using an exploratory factor analysis for Malaysia, we find three clusters of skills from nine digital, cognitive, and socioemotional subsets of skills. Two of these factors are a mix of digital skills and subsets of cognitive and socioemotional skills: 1) basic digital skills with the three subsets of cognitive skills (thinking, communication, and organization), and socioemotional skills related to relationships; and 2) intermediate and advanced digital skills with (to a lesser extent) thinking skills and personal growth. The third cluster does not include digital skills, instead being limited to socioemotional skills, namely with emotions and personal growth. This shows that basic digital skills can be considered a generic skill as much as 35
cognitive and socioemotional and that digital skills are associated to some degree with cognitive and socioemotional skills. Pairwise correlations among those subsets confirm those patterns. Similar patterns emerge for Vietnam, Cambodia, and Thailand.

Our estimations have three main limitations. First, we proxy a skills profile for 20 percent of occupations that are not sufficiently listed in the job postings data set used to create a skills profile. These occupations represent a sizeable share of employment in the four studied countries: from 14 percent in Malaysia to 43 percent in Cambodia. We address this shortcoming by imputing skills profiles for these occupations based on similar occupations. Second, we derive skills use from requirements in job postings, which may omit skills that employers implicitly require rather than explicitly request. Employers do not specify the level of skills proficiency or use, so we equally weight all skills listed in the job posting. As a result, our skills profiles may not be complete, particularly omitting lower-level skills or under- (or over-) weighting the importance of certain skills. Third, we may be over-estimating the degree of digital-ness of the 127 occupations. Since we draw our skills data from on-line jobs vacancy postings, the jobs within each of the 127 occupations that are listed on the internet – and used to construct a skills profile that is a proxy for all jobs within an occupation – may be those that are more inclined to use digital technologies.

Overall, our findings show that all occupations require digital, cognitive, and socioemotional skills, suggesting that policy makers may need to reform the “basic package” provided by education systems in response to digital technologies in the workplace. Education system still need to teach technical and language skills, since those are in very high demand by employers. However, they may also want to rethink the “basic package” of education, namely, to continue teaching cognitive skills, increasing emphasis on socioemotional skills, and adding basic digital skills to the general curriculum. Learning to learn will become increasingly important as new technologies enter the workplace and workers need to adapt to them.

Finally, while advanced digital skills are increasingly emphasized in policy circles, they are only needed for a very small subset of jobs. The growth in demand for these jobs is growing very slowly in Southeast Asia, as comparison to the US. Even as economies grow into the next level of development, advanced skills will still be a minority of the skillset employers value. So, while advanced digital training may be a goal, the reality of the evolving landscape of jobs and technologies is more modest with a continued demand for the traditional cognitive, the increasingly explicit socioemotional, and the newest basic and intermediate digital skills.

36
# **9. References**

- Acemoglu, D., and D. H. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” _In_ O. _Ashenfelter and D. E. Card (Eds.) Handbook of Labor Economics, Volume 4_ : 1043–1171. Amsterdam: Elsevier.

- Aedo, C., J. Hentschel, J. Luque, and M. Moreno. 2013. “From Occupations to Embedded Skills: A Cross-Country Comparison.” _World Bank Policy Research Working Paper_ 6560. Washington, DC: World Bank.

- Almlund, M., A. L. Duckworth, J. J. Heckman, and T. Kautz. 2011. “Personality Psychology and Economics.” _In E. A. Hanushek (Ed.). Handbook of the Economics of Education, Vol. 4_ , 1–181. Amsterdam: North Holland.

- Apedo-Amah, M. C., B. Avdiu, X. Cirera, M. Cruz, E. Davies, A. Grover, L. Iacovone, U. Kilinc, D. Medvedev, F. O. Maduko, S. Poupakis, J. Torres, and T. T. Tran. 2021. “Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World.” _World Bank Policy Research Working Paper_ 9434. Washington, DC: World Bank.

- Apella, I., and G. Zunino. 2018. “Nonstandard Forms of Employment in Developing Countries: A Study for a Set of Selected Countries in Latin America and the Caribbean and Europe and Central Asia.” _World Bank Policy Research Working Paper_ 8581. Washington, DC: World Bank.

- Autor, D. H., F. Levy, and R. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” _Quarterly Journal of Economics_ 118(4): 1279–1333.

Bellemare, M. F. 2015. “A Rant on Estimation with Binary Dependent Variables.” _Personal blog_ .

- ______. 2018. “Causal Inference with Observational Data: Nonstandard Dependent Variables.” _Lecture_ . University of Copenhagen, Denmark.

- Burning Glass Technologies. 2019. “Mapping the Genome of Jobs: The Burning Glass Skills Taxonomy.” _White Paper_ . Boston, MA: Burning Glass Technologies.

- Cammeraat, E., and M. Squicciarini. 2021. “Burning Glass Technologies’ data use in policy-relevant analysis: An occupation-level assessment.” _OECD Science, Technology and Industry Working Papers_ 2021/05. Paris: OECD.

- Caunedo, J., E. Keller, and Y. Shin. 2021. “Technology and the Task Content of Jobs across the Development Spectrum.” _NBER Working Paper_ 28681. Cambridge, MA: National Bureau of Economic Research.

- CSC (Malaysia’s Critical Skills Monitoring Committee). 2019. _Critical Occupations List 2018/2019: Technical Report_ . Kuala Lumpur: TalentCorp. Available at: https://bit.ly/3uZ6G4E

- Cirera, X., A. D. Mason, F. de Nicola, S. Kuriakose, D. S. Mare, and T. T. Tran. 2021. _The Innovation Imperative for Developing East Asia_ . World Bank East Asia and Pacific Regional Report. Washington, DC: World Bank.

- Cunningham, W. 2018. “Meeting the Jobs Challenge in Southeast Asia: Lessons from Cambodia, Myanmar, and Vietnam” in World Bank (eds). _Navigating Uncertainty: East Asia and Pacific Economic Update._ World Bank East Asia and Pacific Regional Report. Washington, DC: World Bank.

37
- Cunningham, W., and P. Villaseñor. 2016. “Employer Voices, Employer Demands, and Implications for Public Skills Development Policy Connecting the Labor and Education Sectors.” _World Bank Research Observer_ 31 (1): 102–34.

- DE4A (Digital Economy for Africa). 2021. _Digital Skills: The Why, the What, and the How - Methodological Guidebook for Preparing Digital Skills Country Action Plans for Higher Education and TVET (V2.0)_ . Washington, DC: World Bank.

- Deming, D. J. 2017. “The Growing Importance of Social Skills in the Labor Market.” _Quarterly Journal of Economics_ 132(4): 1593–1640.

- Deming, D. J., and L. B. Kahn. 2018. “Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals.” _Journal of Labor Economics_ 36(1): 337–69.

- Deming, D. J., and K. Noray. 2020. “Earnings Dynamics, Changing Job Skills, and STEM Careers.” _Quarterly Journal of Economics_ 135(4): 1965–2005.

- DeStefano and Timmis _forthcoming._ “Firm Digital Adoption during COVID-19.” _Manuscript_ . Discussed in World Bank (2021, pp. 28–29).

- Friedman, J. 2012. “Whether to probit or to probe it: in defense of the Linear Probability Model.” _Development Impact Blog_ . Washington, DC: World Bank.

- Goos, M., Manning, A., Salomons, A. 2014. “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring.” _American Economic Review: Papers & Proceedings_ 99(2): 58–63.

- Grundke, R., L. Marcolin, T. L. B. Nguyen, and M. Squicciarini. 2018. “Which skills for the digital era?: Returns to skills analysis.” _OECD Science, Technology and Industry Working Papers_ 2018/09. Paris: OECD.

- Guerra, N., K. Modecki, and W. Cunningham. 2014. “Social-Emotional Skills Development across the Life Span: PRACTICE.” _World Bank Policy Research Working Paper_ 7123. Washington, DC: World Bank.

- Hardy W., R. Keister R., and P. Lewandowski. 2018. “Educational upgrading, structural change and the task content of jobs in Europe.” _Economics of Transition_ 26(2): 201–231.

- IFC (International Finance Corporation). 2019. _Digital skills in sub-Saharan Africa: Spotlight on Ghana._ Washington, DC: IFC.

- ILO (International Labour Organization). 2012. _International Standard Classification of Occupations 2008. Volume 1: Structure, group definitions and correspondence tables_ . Geneva: ILO.

- ITU (International Telecommunication Union). 2020. _Digital Skills Assessment Guidebook_ . Geneva: ITU.

- Lewandowski, P., A. Park, W. Hardy, and Y. Du. 2021. “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data. _World Bank Economic Review (forthcoming)_ .

- Lo Bello, S., M. L. Sánchez-Puerta, and H. Winkler. 2019. “From Ghana to America: The Skill Content of Jobs and Economic Development.” _World Bank Policy Research Working Paper_ 8758, Washington, DC: World Bank.

38
- Marinescu, I. E., and R. Wolthoff. 2020. “Opening the Black Box of the Matching Function: the Power of Words.” _Journal of Labor Economics_ 38(2): 535–68.

- Muller, N., and A. Safir. 2019. “What Employers Actually Want: Skills in demand in online job vacancies in Ukraine.” _World Bank Social Protection & Jobs Discussion Paper_ 1932. Washington, DC: World Bank.

- Muro, M., S. Liu, J. Whiton, and S. Kulkarni. 2017. _Digitalization and the American Workforce_ . Washington, DC: Brookings.

- OECD (Organisation for Economic Co-operation and Development). 2016. “Skills for a Digital World: 2016 Ministerial Meeting on the Digital Economy Background Report.” _OECD Digital Economy Papers 250_ . Paris: OECD.

- ______. 2019. _OECD Skills Outlook 2019: Thriving in a Digital World_ . Paris: OECD.

- O*NET (Occupational Information Network). 2021. _Career Changers Matrix version 20.3_ . Excel file available at: https://bit.ly/3bTfjXh

- Pasquier-Doumer, L., X Oudin, and V. Nguyen Thang. 2017. _The Importance of Household Businesses and the Informal Sector for Inclusive Growth in Vietnam_ . Vietnam Academy of Social Sciences and the French National Research Institute for Sustainable Development.

- Saltiel, F. 2020. “Who Can Work from Home in Developing Countries?” _Covid Economics_ 6. London: Centre for Economic Policy Research (CEPR).

- Squicciarini, M., and H. Nachtigall, 2021. “Demand for AI skills in jobs: Evidence from online job postings.” _OECD Science, Technology and Industry Working Papers_ 2021/03. Paris: OECD.

StataCorp. 2019. _Stata 16 Base Reference Manual_ . College Station, TX: Stata Press.

- UNESCO (United Nations Educational, Scientific and Cultural Organization). 2017. _Digital Skills for Life and Work_ . Report by the Broadband Commission for Sustainable Development. Paris: UNESCO.

- World Bank. 2018. _World Development Report 2018: Learning to Realize Education’s Promise_ . Washington, DC: World Bank.

- ______. 2021. _East Asia and Pacific Economic Update, April 2021: Uneven Recovery_ . Washington, DC: World Bank.

- Weinberger, C. J. 2014. “The Increasing Complementarity Between Cognitive and Social Skills.” _Review of Economics and Statistics_ 96: 849–861.

39
# **Annex 1. Imputations of skills requirements for occupations unrepresented by job postings**

# **data**

## **Table A.1.1. Employment share of the 27 imputed occupations**

|||**Occupation**|<br>|**Employmen**<br>|**t share (%)**<br>||
|---|---|---|---|---|---|---|
|**#**|**Occupation name**|<br>**code**|**Cambodia**|**Malaysia**|**Thailand**|**Vietnam**|
|1|Legislators and senior officials|111|0.5|0.1|0.7|0.4|
|2|Traditional and complementary medicine professionals|223|0.0|0.0|0.0|0.0|
|3|Traditional and complementary medicine associate professionals|323|0.0|0.1|0.0|0.0|
|4|Veterinary technicians and assistants|324|0.0|0.0|0.0|0.0|
|5|Secretaries (general)|412|0.0|0.1|0.0|0.0|
|6|Street and market salespersons|521|4.8|4.2|3.5|7.1|
|7|Market gardeners and crop growers|611|9.6|5.1|21.7|4.8|
|8|Animal producers|612|6.8|0.2|2.5|1.8|
|9|Mixed crop and animal producers|613|0.0|0.0|0.2|0.0|
|10|Forestry and related workers|621|0.4|0.0|0.2|0.0|
|11|Fishery workers, hunters and trappers|622|1.3|0.7|0.8|1.8|
|12|Subsistence crop farmers|631|12.2|0.0|3.1|1.1|
|13|Subsistence livestock farmers|632|3.5|0.0|0.0|0.1|
|14|Subsistence mixed crop and livestock farmers|633|0.0|0.0|0.0|0.1|
|15|Subsistence fishers, hunters, trappers and gatherers|634|1.1|0.2|0.2|0.0|
|16|Wood treaters, cabinet-makers and related trades workers|752|0.9|0.5|0.3|0.8|
|17|Other craft and related workers|754|0.0|0.1|0.5|0.5|
|18|Metal processing and finishing plant operators|812|0.0|0.2|0.3|0.2|
|19|Rubber, plastic and paper products machine operators|814|0.1|0.6|0.6|0.3|
|20|Food and related products machine operators|816|0.3|0.3|0.5|0.2|
|21|Wood processing and papermaking plant operators|817|0.0|0.5|0.1|0.2|
|22|Locomotive engine drivers and related workers|831|0.0|0.0|0.0|0.0|
|23|Ships'deck crews and related workers|835|0.0|0.1|0.1|0.2|
|24|Vehicle, window, laundry and other hand cleaning workers|912|0.7|0.2|0.4|0.1|
|25|Street and related service workers|951|0.0|0.0|0.0|0.1|
|26|Street vendors (excluding food)|952|0.4|0.3|1.1|0.2|
|27|Refuse workers|961|0.4|0.5|0.3|0.3|
|||**Total**|43.0|14.0|37.1|20.3|

_Sources_ : Employment shares are from Cambodia 2020 Socioeconomic Survey, and 2017 employment surveys of Malaysia, Thailand, and Vietnam.

40
**Table A.1.2. Similar occupations used for imputations**

|**#**|**Occupation name**|**Code**|**Code of occupations used for**<br>**imputations**|
|---|---|---|---|
|1|Legislators and senior officials|111|112, 121, 122, 134, 141, 142, 143, 232,<br>242, 243, 261, 331, 332, 333, 335, 422,<br>522,541|
|2|Traditional,and,complementary,medicine, professionals|223|222,224,225,226|
|3|Traditional,and,complementary,medicine,associate, professionals|323|321,322,325|
|4|Veterinary,technicians,and,assistants|324|321,324,325,516,532|
|5|Secretaries, (general)|412|334,421,422,441|
|6|Street,and,market,salespersons|521||
|7|Street,and,related,service,workers|951|512, 522, 524, 911, 941, 962|
|8|Street,vendors, (excluding,food)|952||
|9|Market, gardeners,and,crop, growers|611||
|10|Animal, producers|612||
|11|Mixed,crop,and,animal, producers|613||
|12|Forestry,and,related,workers|621||
|13|Fishery,workers,,hunters,and,trappers|622|921|
|14|Subsistence,crop,farmers|631||
|15|Subsistence,livestock,farmers|632||
|16|Subsistence,mixed,crop,and,livestock,farmers|633||
|17|Subsistence,fishers,,hunters,,trappers,and, gatherers|634||
|18|Wood, treaters,, cabinet-makers, and, related, trades, workers|752|313, 711, 722, 723, 731, 732, 751, 752,<br>811,815,818,821,921,932|
||||311, 312, 313, 315, 325, 421, 422, 441,|
|19|Other, craft, and, related, workers|754|512, 513, 522, 524, 621, 711, 721, 722,<br>723, 732, 741, 742, 753, 813, 818, 821,<br>833,932|
|20|Metal, processing, and, finishing, plant, operators|812|313, 512, 711, 722, 723, 732, 751, 811,<br>813,815,818,932|
||||265, 313, 512, 711, 712, 721, 722, 723,|
|21|Rubber,, plastic, and, paper, products, machine, operators|814|731, 732, 751, 753, 811, 813, 815, 818,<br>821,932|
|22|Food, and, related, products, machine, operators|816|313, 722, 732, 751, 811, 813, 818, 821,<br>932|
|23|Wood, processing,and, papermaking, plant,operators|817|512,722,732,815,818,821,932|
|24|Locomotive,engine,drivers,and,related,workers|831|511,711,811,832,833,931|
|25|Ships', deck, crews, and, related, workers|835|313, 711, 742, 751, 811, 813, 833, 834,<br>931|
|26|Vehicle,,window,,laundry,and,other,hand,cleaning,workers|912|815|
|27|Refuse,workers|961|441,711,712,832,834,921|

_Sources_ : Own elaboration based on ILO (2012) and O*NET (2021). _Notes_ : ILO (2012) lists some related occupations to a given one. O*NET (2021) lists occupations in the United States that have similar skills and experience and which workers could transfer between with minimal additional preparation.

41
**Table A.1.3.  Name of occupations used for imputations**

|**Code**<br>|**Occupation name**<br>|
|---|---|
|112<br>121<br>|Managing directors and chiefexecutives<br>Business services and administration managers<br>|
|122<br>134<br>|Sales,marketing and developmentmanagers<br>Professional services managers<br>|
|141<br>142|Hotelandrestaurantmanagers<br>Retail and wholesale trade managers|
|143|Otherservicesmanagers<br>|
|222<br>224|Nursingand midwifery professionals<br>Pdilii|
|<br>225|arameca practtoners<br>Veterinarians|
|226<br>232|Other healthprofessionals<br>Vocational education teachers|
|242|Administrationprofessionals|
|<br>243|<br>Sales,marketingandpublic relationsprofessionals|
|261<br>265|Legalprofessionals<br>Creative andperformingartists|
|311|Physicaland engineering science technicians|
|312|Mining,manufacturingand construction supervisors|
|313|Process controltechnicians|
|315|Shipand aircraft controllers and technicians|
|321|Medicaland pharmaceuticaltechnicians|
|322<br>324|Nursingand midwiferyassociateprofessionals<br>Veterinary technicians and assistants|
|325|Other health associateprofessionals|
|331|Financialandmathematicalassociate professionals|
|332<br>333|Sales andpurchasingagents and brokers<br>Business services agents|
|334|Administrative and specialised secretaries|
|335|Regulatory government associate professionals<br>|
|421<br>422|Tellers,moneycollectors and related clerks<br>Clitifti k|
|<br>441|ennormaonworers<br>Other clerical support workers|
|511<br>|Travelattendants, conductors and guides<br>|
|512|Cooks|
|513|Waitersandbartenders|
|516|<br>Otherpersonal services workers|
|522|Shopsalespersons|
|<br>524|<br>Other sales workers|
|532<br>41|Personalcare workersin healthservices<br>Pi i k|
|5<br>621|rotectve servces worers<br>Forestry andrelated workers|
|711<br>|Buildingframe and related trades workers<br>|
|712|Buildingfinishers andrelated trades workers|
|721<br>|Sheet and structural metal workers,moulders and welders,and related workers<br>|
|722<br>723|Blacksmiths, toolmakers andrelated trades workers<br>Machinerymechanics and repairers|
|<br>731|<br>Handicraft workers|
|732<br>|Printingtrades workers<br>|
|741<br>742|Electricalequipmentinstallers andrepairers<br>Electronics and telecommunications installers and repairers|
|751|Food processing andrelated trades workers|
|752<br>753|Wood treaters,cabinet-makers and related trades workers<br>Grmntndrltdtrdrkr|
|811|ae a eae aes woes<br>Miningand mineralprocessing plant operators|
|813<br>|Chemicaland photographic products plant andmachine operators<br>|
|815<br>818|Textile,fur and leatherproducts machine operators<br>Otherstationaryplantandmachineoperators|
|821|<br>Assemblers|
|832<br>|Car, vanandmotorcycle drivers<br>|
|833<br>834|Heavytruck and bus drivers<br>Mobile plant operators|
|911|Domestic,hotel and office cleaners and helpers|
|921|Agricultural,forestry andfisherylabourers|
|931<br>|Miningand construction labourers<br>|
|932<br>941|Manufacturinglabourers<br>Foodpreparation assistants|
|<br>962|<br>Otherelementary workers|

_Sources_ : ILO (2012).

42
# **Annex 2. Occupations classified by digital levels, and share of levels of digital skills in each**

## **Table A.2.1 Highly digital occupations**

|**#**|**Occupation name**|**Code**|**Total**|**Share of**<br>**Advanced**|**digital skills**<br>**Intermediate**|**Basic**|**O*NET**<br>**digital**<br>**score**|
|---|---|---|---|---|---|---|---|
|1|Software and applications developers and analysts|251|58.1|47.5|8.0|2.6|78|
|2|Database and network professionals|252|54.7|42.6|8.3|3.9|76|
|3|ICT service managers|133|39.7|23.9|11.7|4.1|59|
|4|ICT operations and user support technicians|351|46.8|23.3|18.6|4.8|68|
|5|Electrotechnology engineers|215|33.1|21.4|8.7|3.0|68|
|6|Electronics and telecommunications installers and repairers|742|27.7|12.3|10.3|5.1|55|
|7|Mathematicians, actuaries and statisticians|212|22.7|8.0|6.6|8.2|61|
|8|Architects, planners, surveyors and designers|216|44.2|7.9|28.5|7.9|54|
|9|Telecommunications and broadcasting technicians|352|26.9|6.1|12.8|8.0|52|
|10|Physical and engineering science technicians|311|24.0|4.3|14.0|5.6|47|
|11|Printing trades workers|732|30.4|3.3|17.5|9.6|48|
|12|Creative and performing artists|265|34.4|2.3|25.1|7.0|45|
|13|Keyboard operators|413|55.6|2.0|2.6|51.0|46|

## **Table A.2.2. Medium digital occupations**

|||||**Share of**|**digital skills**|||
|---|---|---|---|---|---|---|---|
||||||||**O*NET**|
|**#**|**Occupation name**|**Code**|**Total**|**Advanced**|**Intermediate**|**Basic**|**digital**<br>**score**|
|1|Blacksmiths, toolmakers and related trades workers|722|26.9|1.6|23.2|2.0|48|
|2|Other clerical support workers|441|26.7|2.7|2.4|21.7|45|
|3|General office clerks|411|21.6|2.2|2.4|17.1|47|
|4|Numerical clerks|431|20.4|1.8|3.7|14.9|45|
|5|Chemical and photographic products plant and machine op.|813|18.7|3.4|4.2|11.1|47|
|6|<br>Engineering professionals (excluding electrotechnology)|214|18.3|6.0|7.4|4.9|57|
|7|Financial and mathematical associate professionals|331|17.8|1.8|4.5|11.5|42|
|8|<br>Administration professionals|242|17.4|4.3|4.4|8.7|45|
|9|<br>Vocational education teachers|232|16.9|3.9|8.0|5.1|45|
|10|Sales, marketing and public relations professionals|243|15.8|2.0|6.3|7.5|49|
|11|<br>Building frame and related trades workers|711|15.5|1.8|7.5|6.2|32|
|12|Authors, journalists and linguists|264|15.3|2.0|5.8|7.5|46|
|13|<br>Secretaries (general)|412|15.3|2.1|2.1|11.0|46|
|14|Food and related products machine operators|816|15.2|2.7|7.0|5.5|40|
|15|<br>Librarians, archivists and curators|262|14.3|3.5|4.6|6.2|62|
|16|Wood processing and papermaking plant operators|817|14.1|2.4|7.4|4.3|39|
|17|<br>Regulatory government associate professionals|335|13.9|2.4|2.5|9.0|47|
|18|Protective services workers|541|13.8|5.1|1.3|7.4|36|
|19|Administrative and specialised secretaries|334|13.6|2.5|2.7|8.4|47|
|20|Paramedical practitioners|224|13.6|3.5|3.6|6.4|40|
|21|<br>Rubber, plastic and paper products machine operators|814|13.5|2.3|6.7|4.5|37|
|22|<br>Other craft and related workers|754|13.4|2.7|5.1|5.7|40|
|23|Ship and aircraft controllers and technicians|315|13.3|1.9|2.9|8.6|41|
|24|<br>Metal processing and finishing plant operators|812|12.8|2.0|6.1|4.8|38|
|25|Manufacturing labourers|932|12.5|2.1|4.9|5.5|33|
|26|<br>Wood treaters, cabinet-makers and related trades workers|752|12.4|2.3|6.0|4.1|36|
|27|<br>Assemblers|821|12.3|5.8|2.7|3.8|37|
|28|Other health associate professionals|325|12.2|2.5|2.2|7.4|38|
|29|<br>Ships' deck crews and related workers|835|12.1|2.9|4.3|4.9|37|
|30|<br>Other stationary plant and machine operators|818|11.7|2.5|2.8|6.4|43|
|31|<br>Process control technicians|313|11.2|3.2|4.7|3.3|42|
|32|Physical and earth science professionals|211|11.2|3.4|4.0|3.8|54|
|33|Sheet and structural metal workers, moulders and welders<br>()|721|11.0|5.4|3.4|2.3|37|
|34|...<br>Painters, building structure cleaners and related trades w(...)|713|10.7|3.6|2.9|4.2|35|
|35|Handicraft workers|731|10.4|3.9|4.9|1.6|35|

43
# **Table A.2.3. Low digital occupations**

|||||**Share of**|**digital skills**||**O*NET**|
|---|---|---|---|---|---|---|---|
|**#**|**Occupation name**|**Code**|**Total**|**Advanced**|**Intermediate**|**Basic**|**digital**<br>|
||||||||**score**|
|1|Artistic, cultural and culinary associate professionals|343|17.3|0.6|11.3|5.5|40|
|2|Material-recording and transport clerks|432|16.1|1.3|3.3|11.5|43|
|3|Legal, social and religious associate professionals|341|14.1|0.6|0.6|12.8|47|
|4|<br>Mining, manufacturing and construction supervisors|312|13.0|1.0|5.4|6.5|44|
|5|Transport and storage labourers|933|12.7|0.5|3.0|9.2|25|
|6|<br>Manufacturing, mining, construction, and|132|12.6|1.5|4.7|6.4|50|
||distribution managers|||||||
|7|Building finishers and related trades workers|712|12.6|0.8|6.3|5.5|36|
|8|<br>Mining and construction labourers|931|12.1|0.7|6.7|4.7|42|
|9|<br>Business services agents|333|11.9|0.6|2.7|8.5|45|
|10|<br>Client information workers|422|11.9|1.4|2.0|8.5|47|
|11|Refuse workers|961|11.8|1.4|3.3|7.1|33|
|12|Professional services managers|134|11.5|1.6|2.6|7.3|43|
|13|<br>Legislators and senior officials|111|11.1|1.9|2.9|6.4|44|
|14|<br>Finance professionals|241|10.8|1.6|3.6|5.6|47|
|15|<br>Business services and administration managers|121|10.8|1.4|3.0|6.3|45|
|16|<br>Personal care workers in health services|532|10.4|2.0|0.8|7.6|36|
|17|Life science technicians and related associate<br>professionals|314|9.9|1.0|2.4|6.5|41|
|18|<br>Mining and mineral processing plant operators|811|9.7|1.7|2.4|5.7|25|
|19|<br>Life science professionals|213|9.7|2.4|3.1|4.2|49|
|20|<br>Managing directors and chief executives|112|9.5|2.7|2.9|3.9|45|
|21|<br>Sales, marketing and development managers|122|9.5|1.2|3.2|5.0|45|
|22|<br>University and higher education teachers|231|9.3|4.0|2.4|2.9|49|
|23|<br>Traditional and complementary medicine associate|323|9.2|2.0|1.6|5.6|42|
||professionals|||||||
|24|<br>Travel attendants, conductors and guides|511|9.1|0.2|0.6|8.3|33|
|25|Locomotive engine drivers and related workers|831|9.1|1.3|3.1|4.6|32|
|26|Tellers, money collectors and related clerks|421|8.8|2.0|1.3|5.5|46|
|27|Shop salespersons|522|8.7|1.0|2.0|5.7|43|
|28|<br>Electrical equipment installers and repairers|741|8.7|3.0|2.7|2.9|37|
|29|Production managers in agriculture, forestry and<br>|131|8.7|1.1|2.3|5.2|33|
||fisheries|||||||
|30|Social and religious professionals|263|8.6|1.2|1.5|5.8|43|
|31|Machinery mechanics and repairers|723|8.6|1.9|3.8|2.9|39|
|32|<br>Veterinary technicians and assistants|324|8.5|1.7|1.3|5.5|39|
|33|<br>Medical and pharmaceutical technicians|321|8.2|1.7|1.6|4.8|46|
|34|Sports and fitness workers|342|8.0|3.4|0.9|3.7|41|
|<br>35|<br>Nursing and midwifery associate professionals|<br>322|<br>7.2|<br>1.7|<br>0.9|<br>4.6|<br>43|
|36|<br>Cashiers and ticket clerks|523|7.1|1.8|0.8|4.6|35|
|37|Other teaching professionals|235|7.0|1.4|1.2|4.4|45|
|38|<br>Traditional and complementary medicine|223|6.5|1.5|1.5|3.4|44|
||<br>professionals|||||||
|39|<br>Other services managers|143|6.0|1.5|1.5|3.0|46|
|40|Medical doctors|221|4.4|2.5|0.3|1.6|47|
|41|Heavy truck and bus drivers|833|3.6|2.2|0.4|1.0|29|

44
# **Table A.2.4. Very low digital occupations**

|||||**Share of**|**digital skills**||**O*NET**|
|---|---|---|---|---|---|---|---|
|**#**|**Occupation name**|**Code**|**Total**|**Advanced**|**Intermediate**|**Basic**|<br>**digital**<br>**score**|
|1|Sales and purchasing agents and brokers|332|8.5|0.7|1.7|6.2|42|
|2|Garment and related trades workers|753|8.0|0.0|4.5|3.5|23|
|3|Legal professionals|261|7.4|1.2|1.4|4.8|43|
|4|<br>Mobile plant operators|834|7.1|0.3|1.5|5.3|29|
|5|<br>Other health professionals|226|6.9|0.5|2.2|4.2|45|
|6|Child care workers and teachers' aides|531|6.7|1.0|1.5|4.2|29|
|7|Hairdressers, beauticians and related workers|514|5.9|0.9|0.1|4.8|33|
|8|Other elementary workers|962|5.4|0.3|0.9|4.3|36|
|9|<br>Other sales workers|524|5.4|0.9|1.3|3.2|41|
|10|Food preparation assistants|941|5.1|1.0|0.7|3.4|32|
|11|Street vendors (excluding food)|952|4.9|0.7|1.0|3.2|35|
|12|<br>Street and market salespersons|521|4.9|0.7|1.0|3.2|35|
|13|<br>Street and related service workers|951|4.9|0.7|1.0|3.2|35|
|14|Veterinarians|225|4.8|0.5|1.3|3.0|49|
|15|Hotel and restaurant managers|141|4.7|0.5|1.0|3.2|37|
|16|Retail and wholesale trade managers|142|4.6|0.4|0.8|3.4|42|
|17|Waiters and bartenders|513|4.5|1.2|0.4|2.9|35|
|18|Mixed crop and animal producers|613|4.5|1.4|1.0|2.2|27|
|19|<br>Subsistence mixed crop and livestock farmers|633|4.5|1.4|1.0|2.2|27|
|20|Animal producers|612|4.5|1.4|1.0|2.2|27|
|21|<br>Forestry and related workers|621|4.5|1.4|1.0|2.2|27|
|22|<br>Fishery workers, hunters and trappers|622|4.5|1.4|1.0|2.2|27|
|23|Agricultural, forestry and fishery labourers|921|4.5|1.4|1.0|2.2|27|
|24|<br>Subsistence crop farmers|631|4.5|1.4|1.0|2.2|27|
|25|Market gardeners and crop growers|611|4.5|1.4|1.0|2.2|27|
|26|Subsistence livestock farmers|632|4.5|1.4|1.0|2.2|27|
|27|Subsistence fishers, hunters, trappers and gatherers|634|4.5|1.4|1.0|2.2|27|
|28|Car, van and motorcycle drivers|832|4.5|1.3|1.1|2.1|30|
|29|<br>Building and housekeeping supervisors|515|4.4|0.2|0.4|3.7|43|
|30|<br>Textile, fur and leather products machine operators|815|4.0|0.7|1.0|2.3|29|
|31|<br>Vehicle, window, laundry and other hand cleaning workers|912|4.0|0.7|1.0|2.3|29|
|32|<br>Domestic, hotel and office cleaners and helpers|911|3.6|0.1|1.4|2.2|23|
|33|<br>Food processing and related trades workers|751|3.6|0.6|1.1|1.9|33|
|34|<br>Other personal services workers|516|3.2|0.4|0.6|2.2|35|
|35|<br>Secondary education teachers|233|2.9|0.1|0.5|2.4|45|
|36|<br>Nursing and midwifery professionals|222|2.5|0.6|0.2|1.7|41|
|37|Primary school and early childhood teachers|234|2.1|0.3|0.2|1.6|41|
|38|Cooks|512|1.0|0.7|0.0|0.3|33|

_Source_ : Authors’ calculations based on World Bank-Burning Glass Malaysia online job postings data (2016-18). _Notes_ : Darker red colors show higher magnitudes within the “share of digital skills” or “O*Net digital score” column while darker blue colors show lower magnitudes within each set of columns. O*NET digital score, used as a benchmark here, is based on occupations’ three digitalization aspects in measured by O*NET for occupations in the U.S. —computer & electronics, programming, and interacting with computer— following the methodology by Muro et al. (2017).

45
**Annex 3. Replications of Malaysia-focused analyses about the similarity of skills required in very-low, low, medium, and highly digital occupations for the occupational skills profile, Cambodia, Thailand, and Vietnam**

**Table A.3.1. Share of skills categories across digital occupation levels in the occupational skills profile, Cambodia, Malaysia, Thailand, and Vietnam (replication of data behind figure 6)**

||**Oc**|**cupati**<br>**pro**|**onal ski**<br>**file**|**lls**||**Cam**|**bodia**|||**Mal**|**aysia**|||**Tha**|**iland**|||**Vie**|**tnam**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Di**|**gital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupat**<br>**els**|**ion**|**Di**|**gital o**<br>**lev**|**ccupat**<br>**els**|**ion**|**D**|**igital o**<br>**le**|**ccupati**<br>**vels**|**on**|
|**Skills**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|
|**Digital**|4.8|9.9|15.0|38.3|5.0|9.8|15.5|32.7|4.8|9.4|15.9|33.3|4.5|10.1|15.3|37.1|4.6|9.7|14.8|34.4|
|Advanced digital|0.8|1.6|3.0|15.8|1.0|1.2|2.7|8.7|0.8|1.6|3.1|13.2|1.1|1.3|3.1|12.6|1.1|1.3|3.0|14.5|
|Intermediate digital|1.0|2.6|5.0|13.3|1.5|3.2|5.2|17.5|1.1|2.9|3.8|14.3|1.0|3.1|4.8|15.8|1.1|3.0|5.1|13.6|
|Basic digital|2.9|5.6|7.1|9.3|2.5|5.5|7.6|6.5|2.9|5.0|9.0|5.9|2.4|5.7|7.4|8.8|2.4|5.4|6.7|6.2|
|**Cognitive**|10.9|11.9|10.3|9.4|9.9|10.7|10.1|8.6|10.5|11.6|10.1|8.3|10.5|11.0|9.5|8.8|10.3|11.3|10.2|8.9|
|Thinking|0.6|1.3|1.3|2.0|0.4|1.0|1.0|1.6|0.6|1.2|1.2|1.8|0.4|1.1|1.2|1.8|0.4|1.1|1.2|2.0|
|Communication|5.6|6.0|4.7|3.9|5.2|5.6|4.8|3.5|5.5|5.7|4.6|3.4|5.5|5.4|4.4|3.6|5.3|5.6|4.8|3.7|
|Organization|4.4|4.5|4.0|3.4|4.2|3.9|4.2|3.4|4.3|4.5|4.1|3.0|4.4|4.2|3.7|3.2|4.4|4.3|4.1|3.1|
|**Socioemotional**|11.0|7.9|6.4|6.7|10.0|7.1|5.8|7.6|12.2|7.8|5.9|5.5|9.9|7.1|5.8|6.8|9.9|7.2|6.2|6.1|
|Emotions|0.5|0.5|0.6|0.7|0.7|0.3|0.8|0.5|0.4|0.4|0.6|0.4|0.3|0.4|0.6|0.6|0.3|0.4|0.7|0.4|
|Relationships|7.9|5.7|4.3|3.6|5.6|5.4|3.8|3.4|8.9|5.7|4.1|3.3|6.3|5.3|4.0|3.4|6.2|5.4|4.2|3.5|
|Personal growth|2.6|1.7|1.5|2.4|3.7|1.4|1.2|3.7|2.8|1.6|1.2|1.8|3.3|1.4|1.2|2.8|3.4|1.4|1.2|2.2|
|**Other**|73.4|70.4|68.3|45.6|75.0|72.4|68.6|51.2|72.5|71.2|68.1|52.8|75.1|71.8|69.4|47.3|75.2|71.8|68.8|50.6|
|**Total**|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|

_Source:_ Authors’ calculations based on SEAD data.

_Notes_ : “VL”, “L”, “M”, and “H” respectively stands for “Very low”, “Low”, “Medium”, and “High”. The share of skills splits between digital, cognitive, and socioemotional, and other skills. Digital skills levels and cognitive and socioemotional skills subsets are subcategories counted in the total.

46
# **Table A.3.2. Conditional correlations between digital occupation level and cognitive and socioemotional skills requirements (replication of Table 6)**

_Ordinary Least Square (OLS) regressions of digital occupation levels on dummies taking the value of one if the skills count of a skills category of an occupation is above the mean_

# _A. Occupational skills profile_

||||Occup|ation digit|al level||
|---|---|---|---|---|---|---|
|||Low vs.<br>Verylow|<br>Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|<br>High vs.<br>Verylow|
|||(1)|(2)|(3)|(4)|(5)|
|**Cognitive**|Thinking|0.24|0.19|0.23|**0.65***|**2.26*****|
|||(0.15)|(0.13)|(0.13)|(0.26)|(0.38)|
||Communication|**0.28***|-0.19|0.15|-0.13|-0.33|
|||(0.13)|(0.14)|(0.24)|(0.24)|(0.25)|
||Organization|0.24|-0.05|0.05|0.06|-0.45|
|||(0.13)|(0.13)|(0.17)|(0.27)|(0.61)|
|**Socioemotional**|Emotions|-0.01|**0.28***|0.06|0.40|0.91|
|||(0.13)|(0.13)|(0.18)|(0.27)|(0.54)|
||Relationships|**-0.28****|-0.26|-0.25|**-0.58***|-0.45|
|||(0.10)|(0.18)|(0.28)|(0.28)|(0.27)|
||Personal growth|**-0.20***|0.02|0.18|0.13|-0.21|
|||(0.09)|(0.17)|(0.18)|(0.28)|(0.26)|
||Constant|0.45***|1.46***|2.05***|1.22***|0.32|
|||(0.09)|(0.09)|(0.09)|(0.17)|(0.29)|
||N|79|76|48|54|51|
||R-sq|0.30|0.15|0.13|0.24|0.61|

47
# _B. Cambodia and Malaysia_

||||**C**|**ambodia**|||||**Malaysia**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||Occupat|ion digital le|vel<br>|||Occu<br>|pation digit|al level||
|||Low vs.<br>Very low|Medium<br>vs. Low|High vs.<br>Medium|High<br>vs.<br>Low|High vs.<br>Very<br>low|Low vs.<br>Very<br>low|Mediu<br>m vs.<br>Low|High vs.<br>Medium|High vs.<br>Low|High vs.<br>Very<br>low|
|||(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|**Cognitive**|Thinking|0.25|-0.04|0.20|**0.49***|**2.28*****|**0.36***|0.22|0.01|**0.53***|**2.09*****|
|||(0.16)|(0.13)|(0.11)|(0.21)|(0.46)|(0.17)|(0.13)|(0.15)|(0.23)|(0.40)|
||Communication|**0.79*****|**-0.24***|**0.60****|0.04|-0.28|**0.47***|-0.19|0.01|-0.17|-0.09|
|||(0.15)|(0.09)|(0.21)|(0.16)|(0.24)|(0.21)|(0.14)|(0.33)|(0.24)|(0.16)|
||Organization|0.07|**-0.24***|0.01|-0.22|-0.06|0.00|-0.16|0.12|-0.13|-0.60|
|||(0.12)|(0.10)|(0.05)|(0.15)|(0.10)|(0.16)|(0.15)|(0.10)|(0.14)|(0.41)|
|**Socioemotional**|Emotions|-0.06|**0.71*****|-0.03|0.25|0.10|-0.00|**0.48****|-0.30|0.12|0.81|
|||(0.10)|(0.10)|(0.08)|(0.21)|(0.09)|(0.15)|(0.15)|(0.21)|(0.19)|(0.41)|
||Relationships|-0.05|-0.16|**-0.86*****|**-0.75****|-0.09|**-0.30***|-0.35|0.08|**-0.58***|-0.67|
|||(0.10)|(0.15)|(0.21)|(0.24)|(0.08)|(0.14)|(0.18)|(0.38)|(0.24)|(0.43)|
||Personal growth|-0.14|-0.08|**0.46****|**0.60****|-0.07|-0.16|-0.03|**0.36***|0.34|-0.55|
|||(0.12)|(0.18)|(0.15)|(0.19)|(0.08)|(0.16)|(0.17)|(0.18)|(0.20)|(0.41)|
||Constant|0.20|1.46***|2.03***|1.12***|0.08|0.44**|1.47***|2.22***|1.39***|0.74|
|||(0.12)|(0.14)|(0.07)|(0.10)|(0.08)|(0.14)|(0.14)|(0.22)|(0.33)|(0.54)|
||N|79|75|45|52|49|79|75|47|54|51|
||R-sq|0.65|0.66|0.49|0.39|0.59|0.36|0.36|0.13|0.15|0.44|

48
# _C. Thailand and Vietnam_

|||||**Thailand**|||||**Vietnam**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||Occup|ation digit|al level|||Occup|ation digita|l level||
|||Low vs.<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|High vs.<br>Very<br>low|Low vs.<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|High vs.<br>Very<br>low|
|||(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|(25)|
|**Cognitive**|Thinking|0.26|0.21|0.08|0.46|**2.11*****|0.24|**0.25***|0.08|**0.65***|**2.59*****|
|||(0.18)|(0.13)|(0.09)|(0.25)|(0.40)|(0.14)|(0.12)|(0.08)|(0.25)|(0.28)|
||Communication|**0.60***|**-0.26***|0.35|-0.03|-0.15|**0.77*****|-0.25|0.38|0.24|-0.19|
|||(0.23)|(0.12)|(0.29)|(0.20)|(0.14)|(0.17)|(0.16)|(0.29)|(0.25)|(0.15)|
||Organization|0.10|-0.22|0.06|-0.19|-0.45|0.11|**-0.28***|-0.03|-0.42|-0.01|
|||(0.12)|(0.12)|(0.09)|(0.20)|(0.37)|(0.11)|(0.13)|(0.08)|(0.21)|(0.12)|
|**Socioemotional**|Emotions|-0.08|**0.51*****|-0.04|0.29|0.45|-0.14|**0.59*****|0.00|0.29|-0.01|
|||(0.11)|(0.12)|(0.10)|(0.21)|(0.38)|(0.08)|(0.13)|(0.09)|(0.22)|(0.09)|
||Relationships|-0.14|**-0.29***|-0.41|**-0.53***|-0.16|-0.09|-0.43|-0.49|**-0.84****|-0.09|
|||(0.11)|(0.14)|(0.33)|(0.24)|(0.12)|(0.07)|(0.23)|(0.31)|(0.27)|(0.07)|
||Personal growth|-0.13|0.03|0.27|0.37|-0.14|-0.16|0.14|0.20|**0.43***|-0.10|
|||(0.14)|(0.16)|(0.17)|(0.21)|(0.14)|(0.08)|(0.21)|(0.14)|(0.19)|(0.09)|
||Constant|0.22|1.48***|2.03***|1.17***|0.15|0.20*|1.57***|2.03***|1.17***|0.12|
|||(0.13)|(0.11)|(0.09)|(0.14)|(0.14)|(0.08)|(0.11)|(0.06)|(0.16)|(0.10)|
||N|79|76|48|54|51|79|76|48|54|51|
||R-sq|0.45|0.47|0.18|0.23|0.52|0.66|0.48|0.19|0.28|0.65|

## _Source_ : Authors’ calculations based SEAD data.

_Note_ : Independent variables are dummies taking the value of one if the skills count of a skills category of an occupation is above the meian and thus can be interpreted as percentage point increase (or decrease) towards the probability of being a given occupation digital level. Malaysia does not have one of the 127 occupations of the occupational skills profile (paramedical practitioners, ISCO code 224) according its 2017 labor force survey, so its total number of occupations is 126.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 49
**Annex 4. Replications of Malaysia-focused analyses about the complementarities between digital, cognitive, and socioemotional skills for the occupational skills profile, Cambodia, Thailand, and Vietnam**

# **Table A.4.1. Correlations between skills (replications of Table 7)**

_Pairwise correlations based on the number of skills counts of skills categories_

# _A. Occupational skills profile_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|<br>Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate|1.00<br>**0.45*****|1.00||||||||
|Basic|0.05|**0.31*****|1.00|||||||
|Cognitive<br>Thinking|**0.55*****|**0.40*****|**0.39*****|1.00||||||
|Communication|**0.22****|0.11|**0.37*****|**0.65*****|1.00|||||
|Organization|0.14|**0.30*****|**0.44*****|**0.59*****|**0.60*****|1.00||||
|Socioemotional<br>Emotions|0.15|**0.28*****|**0.64*****|**0.46*****|**0.40*****|**0.44*****|1.00|||
|Relationships|0.06|-0.04|0.08|**0.37*****|**0.59*****|**0.49*****|**0.17***|1.00||
|Personalgrowth|0.07|**0.64*****|**0.18****|0.07|0.13|**0.35*****|**0.29*****|**0.16* **|1.00|
|Others|0.02|**0.15* **|**0.33*****|**0.60*****|**0.66*****|**0.56*****|**0.23*****|**0.33*****|0.09|

# _B. Cambodia_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate<br>Basic|1.00<br>**0.31*****<br>0.10|1.00<br>**0.35*****|1.00|||||||
|Cognitive<br>Thinking<br>Communication<br>Organization|**0.34*****<br>0.14<br>0.09|**0.27*****<br>0.07<br>**0.35*****|**0.64*****<br>**0.62*****<br>**0.54*****|1.00<br>**0.68*****<br>**0.53*****|1.00<br>**0.43*****|1.00||||
|Socioemotional<br>Emotions<br>Relationships|0.01<br>-0.09|**0.46*****<br>0.02|**0.48*****<br>**0.33*****|**0.33*****<br>**0.39*****|0.08<br>**0.49*****|**0.31*****<br>**0.16***|1.00<br>**0.45*****|1.00||
|Personal growth|-0.07|**0.15* **|**-0.30*****|**-0.27*****|**-0.25*****|0.06|-0.06|-0.04|1.00|
|Others|0.08|0.12|**0.58*****|**0.62*****|**0.82*****|**0.43*****|0.05|**0.29*****|-0.13|

50
# _C. Thailand_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|<br>Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate<br>Basic|1.00<br>**0.42*****<br>0.12|1.00<br>**0.35*****|1.00|||||||
|Cognitive<br>Thinking|**0.44*****|**0.39*****|**0.60*****|1.00||||||
|Communication<br>Organization|**0.15***<br>0.10|**0.15***<br>**0.27*****|**0.60*****<br>**0.50*****|**0.64*****<br>**0.61*****|1.00<br>**0.51*****|1.00||||
|Socioemotional<br>Emotions|**0.15***|**0.32*****|**0.63*****|**0.59*****|**0.46*****|**0.41*****|1.00|||
|Relationships|0.01|-0.02|**0.25*****|**0.36*****|**0.60*****|**0.28*****|**0.33*****|1.00||
|Personalgrowth|0.03|**0.31*****|**-0.22****|**-0.18****|-0.10|0.11|-0.14|-0.02|1.00|
|Others|0.07|**0.21****|**0.56*****|**0.57*****|**0.80*****|**0.49*****|**0.30*****|**0.36*****|-0.09|

# _D. Vietnam_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|<br> <br>Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate|1.00<br>**0.46*****|1.00||||||||
|Basic|**0.16* **|**0.46*****|1.00|||||||
|Cognitive<br>Thinking|**0.44*****|**0.44*****|**0.67*****|1.00||||||
|Communication|**0.20****|**0.25*****|**0.70*****|**0.72*****|1.00|||||
|Organization|**0.15* **|**0.39*****|**0.55*****|**0.60*****|**0.60*****|1.00||||
|Socioemotional<br>Emotions|0.14|**0.40*****|**0.62*****|**0.57*****|**0.47*****|**0.45*****|1.00|||
|Relationships|-0.00|-0.02|**0.31*****|**0.34*****|**0.52*****|**0.20****|**0.33*****|1.00||
|Personal growth|-0.03|0.07|**-0.32*****|**-0.23*****|**-0.20****|0.05|**-0.27*****|-0.02|1.00|
|Others|0.12|**0.28*****|**0.60*****|**0.69*****|**0.77*****|**0.57*****|**0.32*****|**0.23****|-0.10|

## _Source_ : Authors’ calculations based SEAD data.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 51
# **Table A.4.2. Exploratory factor analysis of skills for the occupational skills profile, Cambodia, Thailand, and Vietnam (replications of Table 8)**

_Exploratory factor analysis based on skills counts of skills categories_

# _A. Occupational skills profile_

|||||**Fac**|**tors**|||
|---|---|---|---|---|---|---|---|
||**Skills category**|**Skillsubset**|**1**|**2**|**3**|**4**|**Uniqueness**|
|1|Digital|Advanced|0.03|-0.03|0.94|0.10|0.11|
|2|Digital|Intermediate|-0.08|0.20|0.48|0.78|0.11|
|3|Digital|Basic|0.09|0.90|0.04|0.11|0.16|
|4|Cognitive|Thinking|0.50|0.39|0.68|0.02|0.13|
|5|Cognitive|Communication|0.79|0.33|0.24|-0.03|0.21|
|6|Cognitive|Organization|0.68|0.41|0.12|0.29|0.27|
|7|Socioemotional|Relationships|0.18|0.83|0.10|0.17|0.24|
|8|Socioemotional|Emotions|0.91|-0.07|-0.02|0.05|0.17|
|9|Socioemotional|Personalgrowth|0.14|0.09|-0.07|0.95|0.07|

# _B. Cambodia_

|||||**Factors**<br>|||
|---|---|---|---|---|---|---|
||**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1|Digital|Advanced|0.24|0.18|0.79|0.29|
|2|Digital|Intermediate|0.09|0.83|0.28|0.23|
|3|Digital|Basic|0.78|0.35|-0.06|0.27|
|4|Cognitive|Thinking|0.85|0.24|0.14|0.20|
|5|Cognitive|Communication|0.87|-0.03|-0.05|0.25|
|6|Cognitive|Organization|0.49|0.52|0.04|0.48|
|7|Socioemotional|Relationships|0.28|0.68|-0.36|0.32|
|8|Socioemotional|Emotions|0.51|0.16|-0.62|0.33|
|9|Socioemotional|Personal growth|-0.54|0.45|-0.12|0.49|

# _C. Malaysia_

|||||**Factors**<br>|||
|---|---|---|---|---|---|---|
||**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1|Digital|Advanced|0.15|0.74|-0.07|0.43|
|2|Digital|Intermediate|0.20|0.87|0.07|0.20|
|3|Digital|Basic|0.76|0.09|-0.26|0.34|
|4|Cognitive|Thinking|0.80|0.36|0.07|0.22|
|5|Cognitive|Communication|0.82|-0.02|0.32|0.23|
|6|Cognitive|Organization|0.80|0.14|0.23|0.29|
|7|Socioemotional|Relationships|0.74|0.16|-0.06|0.42|
|8|Socioemotional|Emotions|0.34|-0.25|0.78|0.22|
|9|Socioemotional|Personal growth|-0.12|0.43|0.75|0.24|

52
# _D. Thailand_

||||**Factors**<br>|||
|---|---|---|---|---|---|
|**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1Digital|Advanced|0.03|0.78|-0.04|0.39|
|2Digital|Intermediate|0.19|0.78|0.35|0.24|
|3Digital|Basic|0.71|0.32|-0.26|0.32|
|4Cognitive|Thinking|0.73|0.49|-0.18|0.19|
|5Cognitive|Communication|0.87|0.02|-0.05|0.25|
|6Cognitive|Organization|0.71|0.20|0.22|0.41|
|7Socioemotional|Relationships|0.67|0.31|-0.20|0.41|
|8Socioemotional|Emotions|0.73|-0.31|0.12|0.36|
|9Socioemotional|Personal growth|-0.07|0.09|0.95|0.08|

# _E. Vietnam_

||||**Factors**<br>|||
|---|---|---|---|---|---|
|**Skills category**|**Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1Digital|Advanced|0.06|0.78|-0.03|0.38|
|2Digital|Intermediate|0.28|0.81|0.09|0.26|
|3Digital|Basic|0.76|0.28|-0.32|0.24|
|4Cognitive|Thinking|0.75|0.43|-0.18|0.22|
|5Cognitive|Communication|0.88|0.08|-0.10|0.21|
|6Cognitive|Organization|0.72|0.30|0.20|0.36|
|7Socioemotional|Relationships|0.66|0.24|-0.31|0.41|
|8Socioemotional|Emotions|0.70|-0.36|0.10|0.37|
|9Socioemotional|Personal growth|-0.10|0.04|0.96|0.07|

_Sources_ : Authors’ calculations based on SEAD data. _Note_ : The factor analysis is based on varimax rotation for orthogonal factors.

53
# **Annex 5. Occupations’ classification in exploratory factor analysis of skills categories**

**Table A.5.1. Occupations’ scores of factors from exploratory factor analysis of digital, cognitive, and socioemotional skills subsets in Malaysia**

|**Occupation**|||||||
|---|---|---|---|---|---|---|
|**Name**|**Code**|**Digitalization**<br>**level**|**Factor**<br>**1**|**Factor**<br>**2**|**Factor**<br>**3**|**Employment**<br>**share in**<br>**Malaysia**|
|Architects, planners, surveyors and designers|216|High|-0.5|7.8|3.9|0.4|
|Software and applications developers and analysts<br>|251<br>|High<br>|0.5<br>|7.2<br>|-0.2<br>|0.3<br>|
|Database and network professionals|252|High|1.3|6.5|-0.6|0.3|
|Creative and performing artists|265|High|-0.3|6.1|3.5|0.1|
|ICT operations and user support technicians|351|High|1.1|4.9|-0.4|0.3|
|Telecommunications and broadcasting technicians|352|High|0.3|4.5|2.3|0.1|
|Information and communications technology service managers|133|High|1.4|4.0|0.4|0.1|
|Electrotechnologyengineers|215|High|04|33|-04|05|
|<br>Printing trades workers|732|High|.<br>-0.5|.<br>2.3|.<br>0.5|.<br>0.1|
|Electronics and telecommunications installers and repairers|742|High|0.1|2.0|-0.3|0.1|
|Mathematicians, actuaries and statisticians|212|High|2.3|1.7|-0.3|0.0|
|Physical and engineering science technicians|311|High|-0.5|1.1|-0.9|2.3|
|Keyboard operators|413|High|2.3|-0.3|-3.3|0.1|
|Regulatory government associate professionals|335|Medium|2.9|-0.1|-0.5|0.9|
|Chemical and photographic products plant and machine operators|813|Medium|2.4|0.4|0.3|0.2|
|Administration professionals|242|Medium|2.3|0.6|0.4|0.3|
|Administrative and specialised secretaries|334|Medium|2.1|-0.2|0.1|0.9|
|Librarians, archivists and curators|262|Medium|1.6|-0.1|1.6|0.0|
|Numerical clerks|431|Medium|1.3|-0.2|-1.3|1.2|
|Authors, journalists and linguists|264|Medium|1.3|2.2|2.6|0.1|
|Sales, marketing and public relations professionals|243|Medium|1.1|1.4|1.3|0.2|
|Secretaries (general)|412|Medium|1.1|-0.5|-0.2|0.1|
|Ship and aircraft controllers and technicians|315|Medium|1.1|-0.2|0.0|0.1|
|General office clerks|411|Medium|1.1|-0.3|-1.5|4.8|
|Financial and mathematical associate professionals|331|Medium|0.9|0.1|-1.2|0.3|
|Engineering professionals (excluding electrotechnology)|214|Medium|0.7|1.2|-0.3|1.0|
|Protective services workers|541|Medium|0.6|-0.2|-0.5|2.8|
|Processcontroltechnicians|313|Medium|06|08|-04|01|
|<br>Other clerical support workers|441|Medium|.<br>0.3|.<br>-0.4|.<br>-1.6|.<br>0.5|
|Ships'deck crews and related workers|835|Medium|0.1|0.4|-0.3|0.1|
|Other health associate professionals|325|Medium|0.1|0.0|-0.9|0.4|
|Physical and earth science professionals|211|Medium|0.0|0.6|-0.1|0.0|
|Building frame and related trades workers<br>|711|Medium<br>|0.0|0.9|-0.8|1.8|
|Other craft and related workers|754|Medium|-0.1|0.2|-0.3|0.1|
|Food and related products machine operators|816|Medium|-0.1|0.7|-0.3|0.3|
|Assemblers|821|Medium|-0.2|0.1|-0.8|1.1|
|Vocational education teachers|232|Medium|-0.2|0.4|-0.5|0.1|
|Metal processing and finishing plant operators|812|Medium|-0.3|0.5|-0.3|0.2|
|Rubber, plastic and paper products machine operators|814|Medium|-0.3|0.8|-0.1|0.6|
|Manufacturing labourers|932|Medium|-0.4|0.2|-0.7|0.5|
|Paramedical practitioners|224|Medium|-0.4|-0.1|-0.6||
|Wood treaters cabinet-makers and related trades workers|752|Medium|-0.5|0.5|-0.4|0.5|
|,<br>Sheetandstructuralmetalworkersmouldersandwelders()|721|Medium|-06|06|-09|09|
|, , ...<br>Painters, building structure cleaners and related trades workers|713|Medium|.<br>-0.6|.<br>-0.1|.<br>-0.9|.<br>0.4|
|Wood processing and papermaking plant operators|817|Medium|-0.7|0.5|-0.5|0.5|
|Handicraft workers|731|Medium|-0.7|0.5|-0.3|0.1|
|Other stationary plant and machine operators|818|Medium|-0.7|-0.2|-0.9|3.2|
|Blacksmiths, toolmakers and related trades workers|722|Medium|-1.3|1.9|-1.4|0.1|
|Finance professionals|241|Low|2.4|0.2|0.0|1.5|
|Mididhifi|112|L|21|00|17|11|
|anagng rectors an ce executves||ow|.|.|.|.|
|Businessservicesandadministrationmanagers|121|Low|2.1|-0.2|0.5|1.5|
|<br>Other services managers|143|Low|2.0|-1.0|3.2|0.2|
|Professional services managers|134|Low|1.8|-0.3|0.3|0.2|
|Manufacturing mining construction and distribution managers|132|Low|1.6|0.0|0.1|0.9|
|,,,<br>Sales, marketing and development managers|122|Low|1.5|0.4|2.1|0.4|

54
|**Occupation**|||||||
|---|---|---|---|---|---|---|
|**Name**|**Code**|**Digitalization**<br>**level**|**Factor**<br>**1**|**Factor**<br>**2**|**Factor**<br>**3**|**Employment**<br>**share in**<br>**Malaysia**|
|Material-recording and transport clerks|432|Low|1.4|-0.2|-0.7|0.9|
|Legal, social and religious associate professionals<br>|341|Low|1.4|-0.6|-1.2|0.2|
|Legislators and senior officials|111|Low|1.4|-0.1|0.6|0.1|
|Clientinformationworkers|422|Low|13|-04|-03|05|
|<br>Business services agents|333|Low|.<br>1.2|.<br>-0.2|.<br>0.7|.<br>0.4|
|Social and religious professionals|263|Low|1.1|-0.6|0.6|0.2|
|Life science professionals|213|Low|1.0|0.0|0.4|0.1|
|Tellers, money collectors and related clerks|421|Low|0.8|-1.1|0.9|0.5|
|Travel attendants, conductors and guides|511|Low|0.6|0.3|1.4|0.1|
|Other teaching professionals<br>|235<br>|Low<br>|0.6<br>|-0.7<br>|2.0<br>|0.7<br>|
|Mining, manufacturing and construction supervisors<br>Transortandstoraelabourers|312<br>933|Low<br>Low|0.5<br>04|-0.2<br>-02|-0.1<br>-10|1.3<br>02|
|p g <br>Shop salespersons|522|Low|.<br>0.4|.<br>-0.3|.<br>0.4|.<br>5.7|
|Production managers in agriculture, forestry and fisheries<br>|131<br>|Low<br>|0.4<br>|-0.3<br>|0.2<br>|0.1<br>|
|Mining and construction labourers|931|Low|0.4|0.1|-0.6|2.2|
|Life science technicians and related associate professionals|314|Low|0.4|-0.1|-0.7|0.1|
|Sports and fitness workers|342|Low|0.3|-0.2|1.4|0.1|
|Personalcareworkersinhealthservices|532|Low|00|-07|-03|02|
|<br>University and higher education teachers|231|Low|.<br>-0.2|.<br>-0.1|.<br>0.5|.<br>0.6|
|Traditional and complementary medicine associate professionals|323|Low|-0.2|-0.3|-1.1|0.1|
|Veterinary technicians and assistants|324|Low|-0.2|-0.5|-0.5|0.0|
|Locomotiveeninedriversandrelatedworkers|831|Low|-02|01|-03|00|
|g <br>Traditional and complementary medicine professionals|223|Low|.<br>-0.3|.<br>-0.5|.<br>-0.4|.<br>0.0|
|Medicalandpharmaceuticaltechnicians|321|Low|-0.3|-0.5|-0.9|0.1|
|<br>Nursing and midwifery associate professionals|322|Low|-0.3|-0.5|-1.4|0.7|
|Mining and mineral processing plant operators<br>|811<br>|Low<br>|-0.3<br>|0.2<br>|-0.5<br>|0.2<br>|
|Medical doctors|221|Low|-0.4|-0.7|-0.1|0.4|
|Artisticculturalandculinarassociaterofessionals|343|Low|-04|25|23|03|
|, y p<br>Refuse workers<br>Cashiersandticketclerks|961<br>523|Low<br>Low|.<br>-0.6<br>-06|.<br>0.1<br>-03|.<br>-0.9<br>-08|.<br>0.5<br>08|
|<br>Machinery mechanics and repairers|723|Low|.<br>-0.8|.<br>0.0|.<br>-0.8|.<br>2.2|
|Building finishers and related trades workers|712|Low|-0.9|0.2|-0.9|0.9|
|Electrical equipment installers and repairers|741|Low|-1.0|0.0|-0.8|1.2|
|Heavy truck and bus drivers<br>|833<br>|Low<br>|-1.1<br>|-0.4<br>|-0.7<br>|3.2<br>|
|Primary school and early childhood teachers|234|Very low|-1.1|-0.9|2.8|1.9|
|Secondary education teachers<br>Retail and wholesale trade managers|233<br>142|Very low<br>Very low|-0.8<br>1.1|-1.1<br>-0.8|2.7<br>1.7|1.4<br>0.5|
|Childcareworkersandteachers'aides|531|Verlow|05|-04|16|16|
|<br>Food processing and related trades workers|751|y <br>Very low|.<br>-0.4|.<br>0.2|.<br>1.3|.<br>1.2|
|Hotel and restaurant managers|141|Very low|1.0|-0.9|1.2|0.5|
|Other sales workers|524|Very low|0.1|-0.7|0.9|1.6|
|Food preparation assistants<br>Sales and purchasing agents and brokers|941<br>332|Very low<br>Very low|0.6<br>0.5|-0.7<br>-0.6|0.8<br>0.6|0.5<br>2.3|
|Bildinndhkinrir|515|Vrl|03|10|05|02|
|ug a ouseeepg supevsos<br>Hairdressers, beauticians and related workers|514|ey ow<br>Very low|.<br>-0.7|-.<br>-0.7|.<br>0.4|.<br>0.8|
|Market gardeners and crop growers<br>|611<br>|Very low<br>|-1.3<br>|0.3<br>|0.3<br>|5.1<br>|
|Animal producers<br>Mixed crop and animal producers<br>|612<br>613<br>|Very low<br>Very low<br>|-1.3<br>-1.3<br>|0.3<br>0.3<br>|0.3<br>0.3<br>|0.2<br>0.0<br>|
|Forestry and related workers|621|Very low|-1.3|0.3|0.3|0.0|
|Fisherworkershuntersandtraers|622|Verlow|-13|03|03|07|
|y , pp<br>Subsistence crop farmers|631|y <br>Very low|.<br>-1.3|.<br>0.3|.<br>0.3|.<br>0.0|
|Subsistencelivestockfarmers|632|Verlow|-13|03|03|00|
|<br>Subsistence mixed crop and livestock farmers|633|y <br>Very low|.<br>-1.3|.<br>0.3|.<br>0.3|.<br>0.0|
|Subsistencefishershunterstraersandatherers|634|Verlow|-13|03|03|02|
|, , pp g<br>Agricultural, forestry and fishery labourers|921|y <br>Very low|.<br>-1.3|.<br>0.3|.<br>0.3|.<br>4.8|
|Waitersandbartenders|513|Verlow|-04|-06|03|26|
|<br>Garment and related trades workers|753|y <br>Very low|.<br>-0.2|.<br>0.3|.<br>0.3|.<br>1.1|
|Cooks|512|Very low|-1.2|-0.3|0.2|1.5|
|Street and market salespersons<br>Sttdltdik|521<br>951|Very low<br>Vl|-0.2<br>02|-0.5<br>05|0.2<br>02|4.2<br>00|
|ree an reae servce worers<br>Street vendors (excluding food)|952|ery ow<br>Very low|-.<br>-0.2|-.<br>-0.5|.<br>0.2|.<br>0.3|

55
|**Occupation**||||||**Employment**<br>|
|---|---|---|---|---|---|---|
|**Name**|**Code**|**Digitalization**<br>**level**|**Factor**<br>**1**|**Factor**<br>**2**|**Factor**<br>**3**|**share in**<br>**Malaysia**|
|Domestic, hotel and office cleaners and helpers|911|Very low|0.0|-0.8|0.0|2.1|
|Veterinarians|225|Very low|-0.1|-0.6|0.0|0.0|
|Other personal services workers|516|Very low|-0.6|-0.6|-0.1|0.1|
|Textile, fur and leather products machine operators|815|Very low|-0.4|-0.2|-0.2|0.1|
|Vehicle, window, laundry and other hand cleaning workers|912|Very low|-0.4|-0.2|-0.2|0.2|
|Legal professionals|261|Very low|1.8|-0.4|-0.3|0.2|
|Other health professionals|226|Very low|0.1|-0.5|-0.4|0.3|
|Car, van and motorcycle drivers|832|Very low|-0.8|-0.2|-0.7|1.5|
|Nursing and midwifery professionals|222|Very low|-0.6|-0.8|-0.7|0.2|
|Other elementary workers|962|Very low|-1.0|-0.3|-1.1|1.5|
|Mobile plant operators|834|Very low|-0.8|-0.3|-1.4|0.8|

_Source_ : Authors’ calculations based SEAD data.

_Notes_ : The factor analysis is based on varimax rotation for orthogonal factors of nine digital, cognitive and socioemotional subsets of skills (three each). Clusters are described in section 6. Darker red and blue colors show higher and lower magnitudes, respectively.

56
# **Annex 6. Distribution of occupations across job postings and employment in the four countries**

## **Table A.6.1. Share of job vacancies and employment by digital occupation level of the 127 occupations**

||**ISCO**|**Share in**<br>||**Share in em**|**ployment**||**Digital**<br>|
|---|---|---|---|---|---|---|---|
|**Occupation name**|**code**|**job**<br>**vacancies**|**Cambodia**|**Malaysia**|**Thailand**|**Vietnam**|**occupation**<br>**level**|
|Software and applications developers and analysts|251|6.0|0.1|0.3|0.1|0.1|High|
|Database and networkprofessionals|252|1.8|0.0|0.3|0.1|0.1|High|
|Information and communications technologyservice managers|133|1.0|0.0|0.1|0.0|0.0|High|
|ICT operations and user support technicians|351|1.7|0.0|0.3|0.2|0.1|High|
|Electrotechnologyengineers|215|1.2|0.0|0.5|0.1|0.2|High|
|Electronics and telecommunications installers and repairers|742|0.1|0.1|0.1|0.3|0.3|High|
|Mathematicians,actuaries and statisticians|212|0.1||0.0|0.0|0.0|High|
|Architects, planners,surveyors and designers|216|2.3|0.0|0.4|0.3|0.1|High|
|Telecommunications and broadcastingtechnicians|352|0.1|0.1|0.1|0.0|0.0|High|
|Physical and engineeringscience technicians|311|1.7|0.2|2.3|0.4|0.3|High|
|Printingtrades workers|732|0.0|0.1|0.1|0.1|0.1|High|
|Creative andperformingartists|265|0.2|0.3|0.1|0.2|0.1|High|
|Keyboard operators|413|0.1||0.1|0.1|0.0|High|
|Engineering professionals(excludingelectrotechnology)|214|5.9|0.0|1.0|0.3|0.5|Medium|
|Assemblers|821|0.1|0.1|1.1|1.6|0.8|Medium|
|Sheet and structural metal workers,moulders and welders(...)|721|0.1|0.9|0.9|0.6|1.0|Medium|
|Protective services workers|541|0.3|1.2|2.8|1.4|0.9|Medium|
|Administrationprofessionals|242|2.7|0.0|0.3|0.3|0.8|Medium|
|Vocational education teachers|232|0.0|0.0|0.1|0.1|0.1|Medium|
|Handicraft workers|731|0.0|1.0|0.1|1.3|0.9|Medium|
|Painters,buildingstructure cleaners and related trades workers|713|0.2|0.1|0.4|0.4|0.3|Medium|
|Librarians,archivists and curators|262|0.0|0.0|0.0|0.0|0.1|Medium|
|Paramedicalpractitioners|224|0.1|0.0||0.0|0.0|Medium|
|Chemical andphotographicproductsplant and machine operators|813|0.0|0.1|0.2|0.2|0.0|Medium|
|Physical and earth scienceprofessionals|211|0.2||0.0|0.1|0.0|Medium|
|Process control technicians|313|0.0|0.0|0.1|0.1|0.1|Medium|
|**Ships' deck crews and related workers**|**835**||0.0|0.1|0.1|0.2|Medium|
|**Other craft and related workers**|**754**||0.0|0.1|0.5|0.5|Medium|
|Other clerical support workers|441|0.1|0.1|0.5|0.4|0.9|Medium|
|**Food and relatedproducts machine operators**|**816**||0.3|0.3|0.5|0.2|Medium|
|Other health associateprofessionals|325|0.4|0.1|0.4|0.3|0.1|Medium|
|Administrative and specialised secretaries|334|5.2|0.2|0.9|0.2|0.2|Medium|
|Other stationary plant and machine operators|818|0.1|0.1|3.2|0.5|0.7|Medium|
|Regulatory government associateprofessionals|335|0.1|0.0|0.9|0.2|0.5|Medium|
|**Woodprocessing andpapermaking plant operators**|**817**||0.0|0.5|0.1|0.2|Medium|
|**Wood treaters, cabinet-makers and related trades workers**|**752**||0.9|0.5|0.3|0.8|Medium|
|**Rubber, plastic andpaperproducts machine operators**|**814**||0.1|0.6|0.6|0.3|Medium|
|General office clerks|411|6.0|2.1|4.8|1.5|0.1|Medium|
|**Secretaries(general)**|**412**||0.0|0.1|0.0|0.0|Medium|
|Manufacturinglabourers|932|1.0|0.4|0.5|1.3|1.3|Medium|
|Sales,marketingandpublic relationsprofessionals|243|4.9|0.2|0.2|0.4|0.5|Medium|
|Authors, journalists and linguists<br>|264<br>|0.5|0.2<br>|0.1<br>|0.1<br>|0.1<br>|Medium<br>|
|**Metalprocessing and finishing plant operators**|**812**||0.0|0.2|0.3|0.2|Medium|
|Shipand aircraft controllers and technicians|315|0.1|0.0|0.1|0.0|0.0|Medium|
|Buildingframe and related trades workers|711|0.0|4.5|1.8|1.2|4.2|Medium|
|Numerical clerks|431|0.8|0.1|1.2|0.5|0.1|Medium|
|Financial and mathematical associateprofessionals|331|5.4|0.6|0.3|1.5|0.4|Medium|
|Blacksmiths,toolmakers and related trades workers|722|0.3|0.2|0.1|0.5|0.1|Medium|
|Universityand higher education teachers|231|0.3|0.0|0.6|0.2|0.2|Low|
|Sports and fitness workers|342|0.1|0.0|0.1|0.1|0.0|Low|
|Electrical equipment installers and repairers|741|0.1|0.4|1.2|0.9|0.5|Low|
|Managingdirectors and chief executives|112|1.9|0.0|1.1|0.1|0.0|Low|
|Medical doctors|221|0.1|0.1|0.4|0.1|0.2|Low|

57
||**ISCO**|**Share in**<br>||**Share in employment**|**Digital**<br>|
|---|---|---|---|---|---|
|**Occupation name**|**code**|**job**<br>**vacancies**|**Cambodia**|<br>**Malaysia**<br>**Thailand**<br>**Vietnam**|**occupation**<br>**level**|
|Life scienceprofessionals|213|0.2|0.0|0.1<br>0.1<br>0.1|Low|
|Heavytruck and bus drivers|833|0.1|0.4|3.2<br>0.7<br>0.9|Low|
|**Traditional and complementary medicine associateprofessionals**|**323**||0.0|0.1<br>0.0<br>0.0|Low|
|Personal care workers in health services|532|0.1|0.0|0.2<br>0.3<br>0.1|Low|
|Tellers,moneycollectors and related clerks|421|0.2|0.5|0.5<br>0.3<br>0.2|Low|
|Machinerymechanics and repairers|723|0.2|1.3|2.2<br>1.6<br>0.9|Low|
|**Legislators and senior officials**|**111**||0.5|0.1<br>0.7<br>0.4|Low|
|Cashiers and ticket clerks|523|0.1|0.1|0.8<br>0.4<br>0.1|Low|
|Nursingand midwiferyassociateprofessionals<br>|322<br>|0.0<br>|0.0<br>|0.7<br>0.2<br>0.3<br> <br> <br>|Low<br>|
|Medical andpharmaceutical technicians<br>Miningand mineralprocessing plant operators|321<br>811|0.2<br>0.0|0.0<br>0.0|0.1<br>0.1<br>0.1<br>0.2<br>0.1<br>0.2|Low<br>Low|
|**Veterinary technicians and assistants**|**324**||0.0|0.0<br>0.0<br>0.0|Low|
|Professional services managers|134|1.0|0.1|0.2<br>0.3<br>0.0|Low|
|Financeprofessionals|241|3.1|0.4|1.5<br>0.2<br>1.3|Low|
|**Traditional and complementary medicineprofessionals**|**223**||0.0|0.0<br>0.0<br>0.0|Low|
|Other services managers<br>|143<br>|0.5<br>|0.1<br>|0.2<br>0.1<br>0.3<br> <br> <br>|Low<br>|
|Manufacturing,mining,construction,and distribution managers|132|4.6|0.0|0.9<br>1.0<br>0.1|Low|
|Business services and administration managers|121|6.7|0.2|1.5<br>0.6<br>0.1|Low|
|Client information workers|422|3.1|0.2|0.5<br>0.5<br>0.2|Low|
|Other teaching professionals|235|0.1|0.3|0.7<br>0.2<br>0.2|Low|
|**Refuse workers**|**961**||0.4|0.5<br>0.3<br>0.3|Low|
|**Locomotive engine drivers and related workers**|**831**||0.0|0.0<br>0.0<br>0.0|Low|
|Material-recordingand transport clerks<br>|432<br>|1.4<br>|0.1<br>|0.9<br>0.8<br>0.3<br> <br> <br>|Low<br>|
|Social and religiousprofessionals|263|0.1|0.1|0.2<br>0.1<br>0.1|Low|
|Sales,marketingand development managers|122|5.2|0.1|0.4<br>0.2<br>0.0|Low|
|Production managers in agriculture,forestryand fisheries|131|0.0|0.0|0.1<br>0.0<br>0.0|Low|
|Mining,manufacturingand construction supervisors|312|0.4|0.4|1.3<br>0.5<br>0.1|Low|
|Shopsalespersons|522|4.7|9.1|5.7<br>7.0<br>5.1|Low|
|Life science technicians and related associateprofessionals|314|0.1|0.0|0.1<br>0.0<br>0.0|Low|
|Buildingfinishers and related trades workers|712|0.0|1.2|0.9<br>1.4<br>0.2|Low|
|Miningand construction labourers|931|0.1|2.5|2.2<br>1.7<br>2.3|Low|
|Legal,social and religious associateprofessionals|341|0.2|0.0|0.2<br>0.1<br>0.1|Low|
|Business services agents|333|0.8|0.2|0.4<br>0.3<br>0.2|Low|
|Artistic,cultural and culinaryassociateprofessionals|343|1.4|0.1|0.3<br>0.2<br>0.1|Low|
|Transport and storage labourers|933|06|10|02<br>11<br>10|Low|
|<br>Travel attendants,conductors andguides|511|.<br>0.0|.<br>0.0|.<br>.<br>.<br>0.1<br>0.1<br>0.0|<br>Low|
|**Marketgardeners and crop growers**|**611**||9.6|5.1<br>21.7<br>4.8|Verylow|
|**Animalproducers**|**612**||6.8|0.2<br>2.5<br>1.8|Verylow|
|**Mixed crop and animalproducers**|**613**||0.0|0.0<br>0.2<br>0.0|Verylow|
|**Forestry and related workers**|**621**||0.4|0.0<br>0.2<br>0.0|Verylow|
|**Fishery workers, hunters and trappers**|**622**||1.3|0.7<br>0.8<br>1.8|Verylow|
|**Subsistence crop farmers**|**631**||12.2|0.0<br>3.1<br>1.1|Verylow|
|**Subsistence livestock farmers**|**632**||3.5|0.0<br>0.0<br>0.1|Verylow|
|**Subsistence mixed crop and livestock farmers**|**633**||0.0|0.0<br>0.0<br>0.1|Verylow|
|**Subsistence fishers hunters trappers andgatherers**|**634**||1.1|0.2<br>0.2<br>0.0|Verylow|
|**, , **<br>Agricultural,forestryand fisherylabourers|921|0.0|4.8|4.8<br>2.8<br>30.0|Verylow|
|Carvan and motorcycle drivers|832|02|26|15<br>33<br>19|Verylow|
|, <br>Legalprofessionals<br>Waiters and bartenders|<br>261<br>513|.<br>0.2<br>02|.<br>0.0<br>07|.<br>.<br>.<br>0.2<br>0.2<br>0.1<br>26<br>10<br>05|<br>Verylow<br>Verlow|
|<br>Child care workers and teachers' aides|<br>531|.<br>0.1|.<br>0.1|.<br>.<br>.<br>1.6<br>0.2<br>0.2|y <br>Verylow|
|Foodpreparation assistants|941|0.3|0.2|0.5<br>0.3<br>0.2|Verylow|
|Hairdressers,beauticians and related workers|514|0.2|0.6|0.8<br>0.8<br>0.7|Verylow|
|Other sales workers|524|0.8|0.8|1.6<br>3.0<br>0.5|Verylow|
|Textilefur and leatherproducts machine operators|815|0.0|0.1|0.1<br>1.3<br>3.7|Verylow|
|, <br>**Vehicle, window, laundry and other hand cleaning workers**|**912**||<br>0.7|<br> <br> <br>0.2<br>0.4<br>0.1|<br>Verylow|
|Cooks|512|01|06|15<br>19<br>07|Verylow|
|Sales andpurchasingagents and brokers|332|.<br>6.1|.<br>0.1|.<br>.<br>.<br>2.3<br>0.5<br>0.1|<br>Verylow|
|**Street and market salespersons**|**521**||4.8|4.2<br>3.5<br>7.1|Verylow|
|**Street and related service workers**|**951**||0.0|0.0<br>0.0<br>0.1|Verylow|
|**Street vendors(excluding food)**|**952**||0.4|0.3<br>1.1<br>0.2|Verylow|

58
|**Otin nm**|**ISCO**|**Share in**<br>**jb**||**Share in em**|**ployment**||**Digital**<br>**tin**|
|---|---|---|---|---|---|---|---|
|**ccupao ae**|**code**|**o**<br>**vacancies**|**Cambodia**|**Malaysia**|**Thailand**|**Vietnam**|**occupao**<br>**level**|
|Foodprocessingand related trades workers|751|0.1|1.2|1.2|1.7|1.3|Verylow|
|Nursingand midwifery professionals|222|0.4|0.2|0.2|0.4|0.1|Verylow|
|Veterinarians|225|0.0|0.0|0.0|0.0|0.0|Verylow|
|Other healthprofessionals|226|0.7|0.1|0.3|0.3|0.1|Verylow|
|Hotel and restaurant managers|141|0.3|0.2|0.5|0.3|0.0|Verylow|
|Otherpersonal services workers|516|0.1|0.1|0.1|0.1|0.4|Verylow|
|Retail and wholesale trade managers|142|0.4|0.0|0.5|0.5|0.1|Verylow|
|Mobileplant operators|834|0.0|0.5|0.8|0.5|0.3|Verylow|
|Other elementaryworkers|962|0.2|0.7|1.5|0.9|0.9|Verylow|
|Primaryschool and earlychildhood teachers|234|0.3|0.8|1.9|1.4|1.5|Verylow|
|Buildingand housekeepingsupervisors|515|0.1|0.0|0.2|0.7|0.5|Verylow|
|Domestic,hotel and office cleaners and helpers|911|0.1|0.6|2.1|1.4|0.7|Verylow|
|Secondaryeducation teachers|233|0.1|0.5|1.4|0.6|1.0|Verylow|
|Garment and related trades workers|753|0.0|9.3|1.1|0.6|2.1|Verylow|

_Source:_ Authors’ calculations based on SEAD data.

_Notes_ : The 27 occupations in bold are the ones whose skills profile is imputed from one or several similar occupations (see section 3).

59
# **Annex 7. Robustness Checks: Results using the 5 clusters based on ascending digital occupation levels without merging clusters 2 and 3**

Table A.7.1 and figures A.7.1. and A.7.2. show that the results presented in Tables 6 and Figures 6 and 7 are robust to merging the clusters based on ascending digital occupation levels.

## **Table A.7.1. Conditional correlations between clusters based on ascending digital occupation levels and cognitive and socioemotional skills requirements (replication of Table 6)**

|||||Clus<br>|ters based o<br>|n ascendin<br>|g digital oc<br>|cupation le<br>|vels<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||2 vs. 1|3 vs. 1|4 vs. 1|5 vs. 1|3 vs. 2|4 vs. 2|5 vs. 2|4 vs. 3|5 vs. 3|5 vs. 4|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Cognitive**|Thinking|0.51|**0.82***|**1.35*****|**2.78*****|0.06|**0.78****|**1.43****|0.14|0.38|0.01|
|||(0.26)|(0.40)|(0.27)|(0.53)|(0.31)|(0.24)|(0.47)|(0.11)|(0.39)|(0.15)|
||Communication|0.21|**0.91***|0.18|-0.12|0.33|-0.19|-0.43|-0.25|-0.52|0.01|
|||(0.16)|(0.42)|(0.27)|(0.21)|(0.18)|(0.46)|(0.71)|(0.15)|(0.35)|(0.33)|
||Organization|-0.11|0.30|-0.27|-0.81|0.26|0.16|1.25|**-0.35****|-0.43|0.12|
|||(0.19)|(0.37)|(0.27)|(0.55)|(0.22)|(0.35)|(0.76)|(0.12)|(0.34)|(0.10)|
|**Socioemotional**|Emotions|0.30|-0.35|**1.30*****|1.08|-0.33|0.78|0.04|**0.49*****|0.47|-0.30|
|||(0.16)|(0.33)|(0.25)|(0.54)|(0.19)|(0.44)|(0.66)|(0.11)|(0.33)|(0.21)|
||Relationships|**-0.43****|-0.12|**-1.44*****|-0.89|0.26|-0.60|-1.00|-0.39|**-0.80***|0.08|
|||(0.14)|(0.25)|(0.30)|(0.57)|(0.26)|(0.54)|(0.53)|(0.19)|(0.30)|(0.38)|
||Personal growth|**-0.32***|0.09|**-1.09*****|-0.73|-0.05|-0.76|-0.92|-0.13|0.22|**0.36***|
|||(0.13)|(0.30)|(0.29)|(0.54)|(0.31)|(0.48)|(0.68)|(0.17)|(0.31)|(0.18)|
||Constant|1.48***|1.17***|2.49***|1.98**|2.22***|2.90***|2.69***|3.79***|4.11***|4.22***|
|||(0.15)|(0.26)|(0.33)|(0.72)|(0.13)|(0.36)|(0.65)|(0.11)|(0.61)|(0.22)|
||N|58|59|72|51|41|54|33|55|34|47|
||R-sq|0.34|0.50|0.67|0.44|0.36|0.34|0.26|0.55|0.36|0.13|

_Sources_ : Authors’ calculations based on SEAD data for Malaysia.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224). See subsection 3.2. for details on the clustering.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 60
<!-- Start of picture text --> 056463 6201<br>35<br>g 30<br>z 25<br>e 20<br>5 10<br><<br>0<br>Total Advanced digital Intermediate Basic digital Total Thinking Communication Organization Total Emotions Relationships — Personal growth<br>digital<br>Digital Cognitive Socioemotional<br><!-- End of picture text --> <!-- Start of picture text --> 0.8 9 15<br>13.7 aaa Ws 17.2<br>30 24.4<br>80 16.0 a<br>16.4 a by<br>ec<br>a 70 L |<br>E 3.8 18.1 Ad<br>o - os<br>a 60 5.9<br>5 m4<br>we 50 ms<br>2 12.6<br>wmo «40 2<br>65.6 65.0 Hl<br>20 40.2<br>10<br>0<br>Cambodia Malaysia Thailand Vietnam<br><!-- End of picture text -->