Click, Code, Earn : The Returns to Digital Skills
Overview & Synopsis
This paper provides the first comprehensive, cross-country evidence on the wage returns to digital skills, using more than 67 million job postings from 29 countries between 2021 and 2024. The paper develops a harmonized digital skills taxonomy and examines returns across extensive (any digital skill required), intensive (number of digital skills), and qualitative (type of digital skill) margins. Digital skills command substantial wage premiums globally, with particularly pronounced returns in low- and middle-income countries where such competencies remain scarce. Requiring at least one digital skill raises advertised wages by 1.6 percent on average, with returns of 1.3 percent in high-income countries and 7.5 percent in low- and middle-income countries. Each additional digital skill increases wages by 0.5 percent in high-income countries and 2.6 percent in low- and middle-income countries. Intermediate and advanced skills yield even higher premiums of 0.8 percent in high-income countries and 3 percent in low- and middle-income countries. Each traditional artificial intelligence skill offers returns of 2.9 percent across all countries. Most remarkably, generative artificial intelligence skills demonstrate the highest premiums: wage increases of 7 to 9 percent in technical occupations, and sizable premiums of 25 to 36 percent for generative artificial intelligence literacy skills in nontechnical roles, reflecting both their productivity potential and current scarcity. Returns are consistently higher in digitally-intensive industries and occupations and are amplified by workersโ education and experience, suggesting strong complementarities between digital competencies and traditional human capital. These findings highlight the critical importance of digital skills for individual earnings and economic development, particularly in low- and middle-income countries.
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Document Information
| Document Title | Click, Code, Earn : The Returns to Digital Skills |
|---|---|
| Author / Organization | Soares Martins Neto, Antonio, Liu, Yan, Khunara, Saloni, Porras Lopez, Juan Manuel |
| Subject & Discipline | FY17 - Other Information and Communications Technologies |
| Categories | Economics & Development |
| Publication Year | 2026 |
| License / Rights | Creative Commons Attribution 4.0 (CC BY 4.0) |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
World Bank Documents & Reports
(documents.worldbank.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-173085 |
| Original Source Link | Visit Source Page on documents.worldbank.org » |