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.

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