# 2. Data and descriptive evidence

## a. Data and measurement

Our worker-level dataset covers 47 countries at different development levels (Table A4 in the Appendix). Most of the country coverage comes from the OECD’s Programme for the International Assessment of Adult Competencies – PIAAC (2019). During three rounds of the study (2011-2012, 2014-2015, and 2017-2018), data were collected in 37 countries. The sample sizes amount to a few thousand 16-65 years old individuals. We complement PIAAC with the Skills Towards Employment and Productivity – STEP (World Bank, 2017) survey data from nine low- and middle-income countries. The STEP data were collected in 2012-2014 among urban residents aged 15-64 and covered a few thousand respondents in each country. We also use the “skill use at work” module of the third wave of the China Urban Labour Survey (CULS, 2017), which directly implemented the STEP questionnaire and ensured comparability with other countries in our sample. The survey collected data from six large cities in China (Guangzhou, Shanghai, Fuzhou, Shenyang, Xian, and Wuhan) and covered about 15,000 individuals.

Following Lewandowski et al. (2022), we create a worker-level task content measure of occupations across countries in the spirit of Acemoglu and Autor (2011). As the STEP surveys are urban surveys, for comparability we omit farmers and skilled agricultural workers (ISCO 6 from the sample in all countries. For methodological details, see Lewandowski et al. (2022). We calculate the worker-level routine task intensity according to the following formula: where, rcog, nranalytical, nrpersonal are routine cognitive, non-routine cognitive analytical, and non-routine cognitive personal task levels. Table A1 in Appendix A enlists survey items used to construct these task measures. Particular task measures and RTI are standardized using their mean and standard deviation in the United States.

We use hourly wages in US dollars, adjusted for purchasing power parity, with a 99% winsorization. Wage data are available for 38 of the 47 countries in our sample (Table A4), we adjust the sample accordingly in the wage analysis.<sup>6</sup> The country-sector level measures of GVC participation are based on the EORA database (Lenzen et al., 2012, 2013) and computed following the methodology of Borin & Mancini (2015, 2019). In particular, we use both backward and forward GVC participation measures. Both quantify value-added flows that cross at least two country borders. Backward GVC participation measures the share of imported inputs used in export production (% of total exports). Forward GVC participation captures the share of domestic value added embodied in a country’s bilateral partners’ exports (% of total exports).<sup>7</sup> Finally, we follow Blinder & Krueger, 2013), dividing occupations into offshorable and non-offshorable. We assign occupations to groups starting at the 4-digit ISCO-08 level, depending on data availability. Most of the countries report occupations using 3- and 4-digit ISCO-08 codes. For clarity, Table A3 in Appendix A lists occupations with assigned offshorability groups (at the 2-digit ISCO level).

> 6 We use the full sample of 47 countries in the first part of the analysis to maximize variation across countries. 7 This measure avoids a double-counting problem prevalent in alternative measures of forward GVC participation.

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