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PRWP

Reproducibility package for Click, Code, Earn: The Returns To Digital Skills

2026
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Reference ID
RR_WLD_2025_451
Author(s)
Yan Liu, Antonio Martins Neto, Saloni Khurana, Juan Manuel Porras
Collections
World Bank Policy Research Working Papers
Metadata
JSON
Created on
Jan 30, 2026
Last modified
Jan 30, 2026
  • Project Description
  • Downloads
  • Overview
  • Reproducibility Package
  • Description
  • Scope and coverage
  • Disclaimer
  • Access and rights
  • Contacts
  • Information on metadata
  • Overview

    Abstract

    This paper provides the first comprehensive, cross-country evidence on the wage returns to digital skills using over 67 million job postings from 29 countries between 2021 and 2024. We develop a harmonized digital skills taxonomy and examine 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 (LMICs) where such competencies remain scarce. Requiring at least one digital skill raises advertised wages by 1.6% on average, with returns of 1.3% in high-income countries (HICs) and 7.5% in LMICs. Each additional digital skill increases wages by 0.5% in HICs and 2.6% in LMICs, while intermediate and advanced skills yield even higher premiums of 0.8% in HICs and 3% in LMICs. Each traditional AI skills offer returns of 2.9% across all countries. Most remarkably, generative AI (GenAI) skills demonstrate the highest premiums: GenAI development skills command 7-9% wage increases in technical occupations, while GenAI literacy skills yield sizable premiums of 25-36% in non-technical professional roles, reflecting both their productivity potential and current scarcity. Returns are consistently higher in digitally intensive industries and occupations, and are amplified by workers' 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 LMICs.

    Reproducibility Package

    Scripts
    Readme Get Reproducibility Package
    Link: https://reproducibility.worldbank.org/catalog/451/download/1285/README.pdf
    Reproducibility package for Click, Code, Earn: The Returns To Digital Skills
    File name
    RR_WLD_2025_451
    Zip package
    RR_WLD_2025_451.zip
    Title
    Reproducibility package for Click, Code, Earn: The Returns To Digital Skills
    Date
    2026-01
    Dependencies
    Stata dependencies are listed in the ado folder.
    Instructions
    See README in reproducibility package.
    Notes
    Computational reproducibility verified by Development Impact (DECDI) Analytics team, World Bank.
    Source code repository
    Repository name URI
    Reproducible Research Repository (World Bank) https://reproducibility.worldbank.org
    Software
    Stata
    Name
    Stata
    Version
    18.5 MP

    Reproducibility

    Technology environment

    Paper exhibits were reproduced on a computer with the following specifications:
    • OS: Windows 11 Enterprise
    • Processor: INTEL(R) XEON(R) PLATINUM 8562Y+ 2.80 GHz (2 processors)
    • Memory available: 128 GB

    Technology requirements

    Run time: ~ 2 days

    Reproduction instructions

    Replication of this package was conducted in part through virtual verification due to data access restrictions. To reproduce the findings in this paper, a replicator must:

    1. Secure Access to Data: Access the datasets not included in the package. See subsection Datasets for more details.
    2. Download and Place Data: Once the data is accessed, users should place it in the appropriate folder.
    3. Run the Package: After placing the data in the folder, update the global in line 31 of the do-file "Master" to your folder's location and run the do-file.

    Since all the data is not included, the package includes the results produced by replicators. These files can be used to review the results presented in the paper. A subset of results was verified via virtual verification, and the corresponding verification outputs are also included in the package.

    Data

    Datasets
    Lightcast Global Job Postings
    Name
    Lightcast Global Job Postings
    Note
    The Lightcast Global Job Postings Database is a proprietary dataset containing worldwide online job postings collected from multiple sources. It includes detailed metadata on occupations, industries, skills, employers, wages, and posting timelines. All files listed below are filtered, aggregated, or harmonized derivative datasets created using Databricks (PySpark) and Stata processing scripts. See README for more information. Files: v21_developed_countries_ds_skills_by_frequencies_non_null_sample.dta; v21_developing_countries_ds_skills_by_frequencies_non_null_sample.dta; v21_isco_title_names_by_top_frequencies.csv; v21_lightcast_original_postings_by_country_year.dta; v21_lightcast_postings_by_country_2021_2024.dta; v21_lightcast_postings_by_isco_and_naics_2021_2024.dta; v21_lightcast_postings_by_occupation_2021_2024.dta; v21_lightcast_postings_by_sector_2021_2024.dta; v21_non_null_data_2_dataset_2021_2024_traditional_ai_v4.csv; v21_sample_country_trends.dta; ai_skills_mapping_03.csv; Lightcast_digital_skills_mapping_rds.csv; Lightcast_soft_skills_mapping_rds.csv
    Access policy
    Data access was granted directly to the study authors by the data owners/managers. It was obtained with a custom data license that does not allow for redistribution and it is not included in the reproducibility package.
    Citation
    Lightcast (2025). Lightcast Global Job Postings Database [Proprietary dataset]. Accessed June–July 2025 under institutional license.
    IT-Intensity Dataset (Derived from OECD ICIO and TiVA)
    Name
    IT-Intensity Dataset (Derived from OECD ICIO and TiVA)
    Note
    This dataset contains sector-level measures of information technology (IT) intensity by country and industry. It is author-constructed using value-added components from the OECD Inter-Country Input-Output (ICIO) Tables and Trade in Value-Added (TiVA) Database, harmonized to NAICS 2-digit industries using an author-developed concordance. The resulting measures are used as inputs in the empirical analysis. Files: sector.dta; tiva_naics2.dta
    Access policy
    Data is publicly available and included in the reproducibility package.
    Citation
    1. OECD. 2023. OECD Inter-Country Input-Output (ICIO) Tables and Trade in Value-Added (TiVA) Database [Data sets]. OECD. https://data-explorer.oecd.org/. 2. Boy, H. C. 2023. sectors.dta [Author-constructed dataset]. World Bank Group. 3. World Bank Group. 2024. tiva_naics2.dta [Author-constructed dataset].
    World Bank Income Classification
    Name
    World Bank Income Classification
    Note
    This dataset provides a country-level comparison of the World Bank income classification between fiscal years 2025 and 2026. It is constructed by the authors using publicly available World Bank income classification data and maps changes in country income groups across the two fiscal years. Files: wb_income_fy26_25_comparison.dta
    Access policy
    Data is publicly available and included in the reproducibility package.
    License
    Creative Commons Attribution 4.0 (CC-BY 4.0)
    License URL
    https://datacatalog.worldbank.org/public-licenses#cc-by
    Data URL
    https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
    Citation
    World Bank. 2025. Country and Lending Groups [dataset]. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed 2025.
    ILO Labor-Force Data
    Name
    ILO Labor-Force Data
    Note
    This dataset contains annual labor-force indicators (employment, labor-force participation, working-age population 15+) sourced from the ILOSTAT database. Files: ILO_labor_force_data.dta
    Access policy
    Data is publicly available and included in the reproducibility package.
    License
    Creative Commons Attribution BY 4.0 licence (CC BY 4.0)
    License URL
    https://www.ilo.org/rights-and-permissions
    Data URL
    https://rshiny.ilo.org/dataexplorer71/
    Citation
    International Labor Organization. (2025). ILOSTAT: Labor force by sex and age (thousands), annual [Data set]. Retrieved June 2025 from https://rshiny.ilo.org/dataexplorer71/
    Data statement

    Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.

    Description

    Output
    Click, Code, Earn: The Returns To Digital Skills
    Type
    Working Paper
    Title
    Click, Code, Earn: The Returns To Digital Skills
    Description
    Policy Research Working Papers (PRWP)
    Authors
    Author Affiliation Email
    Yan Liu World Bank yanliu@worldbank.org
    Antonio Martins Neto World Bank asmartins@worldbank.org
    Saloni Khurana World Bank skhurana@worldbank.org
    Juan Manuel Porras World Bank jporraslopez@worldbank.org
    Date of production

    2026-01-27

    Scope and coverage

    Geographic locations
    Location Code
    World WLD
    Keywords
    Digital skills Online job postings Artificial intelligence Skill demand Wage premiums
    Topics
    ID Topic Parent topic ID Vocabulary Vocabulary URI
    J23 Labor Demand J2 Journal of Economic Literature (JEL)
    J24 Human Capital; Skills; Occupational Choice; Labor Productivity J2 Journal of Economic Literature (JEL)
    J31 Wage Level and Structure; Wage Differentials J3 Journal of Economic Literature (JEL)
    O33 Technological Change: Choices and Consequences; Diffusion Processes O3 Journal of Economic Literature (JEL)

    Disclaimer

    Disclaimer

    The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.

    Access and rights

    License
    Name URI
    Modified BSD3 https://opensource.org/license/bsd-3-clause/

    Contacts

    Contacts
    Name Affiliation Email
    Yan Liu World Bank yanliu@worldbank.org
    Reproducibility WBG World Bank reproducibility@worldbank.org

    Information on metadata

    Producers
    Name Abbreviation Affiliation Role
    Reproducibility WBG DECDI World Bank - Development Impact Department Verification and preparation of metadata
    Date of Production

    2026-01-27

    Document version

    1

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