World Bank Data Dive - December 3rd 2025

Agenda:

12:00pm: Lunch & networking

1:00pm: Opening by WB Co-Hosts & Keynote

1:30pm: Challenge overviews

2:00pm: Hackathon

6:00pm: Submissions / Team Presentations

7:30pm: Awards and closing

Presentation website: https://datacommunitydc.github.io/DataDive25/

Submissions: https://github.com/datacommunitydc/DataDive25

(Full submission instructions lower down the page)

 

Agenda

11:00 - 12:00: Doors Open & Security Screening

12:00 - 1:00: Lunch & networking

1:00 - 1:30: Opening by WB Partners + keynote

1:30 - 1:45: Challenge overviews

1:45 - 2:00: Launch / announcement(s)

2:00 - 6:00: Working time (refreshments available throughout)

6:00 - 6:15: Submissions

6:15 - 7:15: Team presentations

7:15 - 7:30: Judge Deliberations / People’s vote.

7:30 - 8:00: Awards and closing

2025 Development Data Dive (Five Challenge Categories!):

Category I: Toward Pro-Jobs Business Environments. Discover how smarter regulations, better infrastructure, and stronger firm capabilities can unleash massive job creation across developing economies. Dive into rich cross-country data to pinpoint what really drives firms to grow, and what holds them back! Help shape the evidence for the next generation of pro-jobs reforms.

  • Challenge 1: How does the business environment shape firm dynamics and job outcomes? Identify which business-environment factors (regulation, infrastructure, finance, corruption, firm capabilities) most strongly enable or constrainjob creation—especially for specific firm types (SMEs, young firms, exporters, women-led firms) and across income levels.

    • CONTEXT: Private sector accounts for most job creation (World Bank 2024)—but firms’ ability to expand employment depends heavily on the quality of the business environment. High regulatory burdens, unreliable infrastructure, poor access to finance, weak managerial capabilities, and low competition, can all suppress firm productivity and limit job creation. Additionally, these mediating factors may work differently across different levels of economic development and have differential impact on firms of various characteristics (such as size, economic sector, exporting, or ownership type etc.). As governments aim to foster more dynamic, job-rich private sectors, evidence on how the business environment shapes firm performance is critical.

    • DATA SOURCES:

      • World Bank Enterprise Surveys (WBES, https://login.enterprisesurveys.org/). Please consider using the “combined data” tab to access cross-country harmonized data and indicators on employment, growth rates, productivity indicators, labor costs, firm characteristics, business environment measures acrossmany topics including access to finance, taxes, infrastructure, competition, corruption, management practices, innovations, along with the perception data on constraints to firm’s operations. Please also consider using the Employment Indicators of the WBES https://www.enterprisesurveys.org/en/employment-indicators.

      • Business Ready database

      • Data360: Growth & Jobs (Employment trends, by sector).

  • Challenge 2: How do regulations impact job outcomes and firm performance? Analyze how the quality of a country’s regulations—both the rules on the books and firms’ real-world experiences—shapes firm growth and job creation. Identify which regulatory reforms (e.g., business entry, trade, electricity, taxes) most improve hiring, reduce skills constraints, boost formalization, and support stronger employment outcomes across different types of firms.

    • CONTEXT: Regulations that are overly complex, ambiguous, or poorly designed can raise compliance costs for firms, discourage formalization, and limit hiring. By contrast, high-quality regulations (those that are clear, coherent, and aligned with firms’ operational realities) can lower barriers to growth and support the creation of more and better jobs. Understanding how differences in regulatory quality across countries influence firm behavior and employment outcomes is essential for designing reforms that foster private-sector development.

    • DATA SOURCES:

  • Challenge 3: Do earnings explain variation in job quality? Use cross-country and sectoral data to determine how strongly earnings predict overall job quality—such as benefits, security, and working conditions—and how this relationship changes across time, development levels, and governance environments. Identify where higher pay reliably signals better jobs, where it doesn’t, and why.

    • CONTEXT: A large body of empirical research shows a clear and positive relationship between earnings and many non-wage dimensions of job quality—including contract security, benefits, and working conditions. As workers move into higher-paying jobs, they are more likely to gain stability, protections, and better overall employment conditions. What remains less explored are the nuances and limits of this relationship: how strongly earnings correlate with job quality across different countries and sectors, how the relationship evolves over time, and how institutional and governance contexts shape it. This challenge invites participants to examine these patterns in greater depth, identifying where the link between pay and job quality is strongest, where it diverges, and what factors may help explain that variation.

    • DATA SOURCES:

Category II: Jobs & Gender. Unlock the hidden dynamics behind women’s employment: how do public-sector pathways, legal rights, and workplace protections widen or close gender gaps? Use cutting-edge datasets to map where women’s economic opportunities are expanding or stalled. Generate insights that could redefine how countries design gender-equal labor markets.

  • Challenge 4: Women in public sector jobs: crowding out or in? Assess whether women’s high public-sector employment reduces private-sector opportunities (via queuing, wage expectations, talent diversion) or expands them (via norm shifts, spillovers).

    • CONTEXT: Across many developing countries, women disproportionately work in the public sector, where jobs often offer more predictable wages, better work–family balance, and less discriminatory pay practices (World Bank, 2021). Public institutions typically follow stronger regulatory and pay-equity frameworks, which make them more attractive to women than private employers. As a result, women’s public-sector employment shares are substantially higher than men’s across most regions. The macro-labor implications of this pattern remain unclear. On one hand, large-scale public-sector hiring of women could “crowd out” female employment in the private sector by drawing skilled women away from private firms, driving up reservation wages, or reducing the supply of qualified female candidates. On the other hand, women’s visibility in public employment may “shift” social norms, reduce gender barriers, and create positive spillovers, encouraging more women to enter the labor force and transition into non-traditional, higher-productivity private-sector occupations.

    • DATA SOURCES:

  • Challenge 5: How do laws shape women’s jobs and entrepreneurship? Examine how legal gender gaps (workplace protections, pay equity, entrepreneurship, parenthood) relate to women’s labor-force participation, earnings, sectoral mobility, and business performance.

Category III: Jobs & Youth. Explore the future of the world’s youngest workforce: their education levels, their readiness for AI, and the skills that will shape prosperity in 2035. Reveal which countries are racing ahead and which risk being left behind. Build bold, data-driven narratives that illuminate the opportunities and vulnerabilities facing a generation.

  • Challenge 6: Exploring the future of education & jobs: what youth skills will matter most in 2035? Track how youth education levels (15–24) will shift by 2035 across countries and scenarios, and what this implies for future skills, gender gaps, and regional winners & who may fall behind.

    • CONTEXT: The world’s young people (ages 15-24) are the workforce, entrepreneurs, and leaders of tomorrow. But how educated will they be? Where will academic skills improve most dramatically, and where might population growth outpace educational gains? We are trying to unearth insights from future scenarios. However, also keep in mind what the data may be hiding. The levels and years of education are standardized, but the quality of the education is not.

    • METRICS YOU MIGHT USE: You’re free to choose and combine metrics creatively:

      • Average years of schooling (simple, comparable metric)

      • % with tertiary education (focusing on high skills)

      • % with no/incomplete primary (focusing on education access)

      • Education distribution shifts (comparing 2025 vs. 2035 histograms)

      • Population-weighted education (total skilled people, not just rates)

      • Gender parity indices (female/male ratios at different education levels)

      • Education velocity (rate of change in years of schooling)

      • Skill-population divergence (where population grows faster than education)

    • DATA SOURCES:

Category IV: The Public-Private Jobs Nexus. Unpack the high-stakes interaction between public-sector hiring and private-sector productivity, where wage premiums, queuing, and talent competition can reshape entire labor markets! Visualize how worker preferences and firm constraints collide across countries and regions. Provide insights that help governments rebalance incentives and unlock private-sector growth.

  • Challenge 7: Public-sector pull vs. private-sector productivity: how will public-sector labor markets shape private-sector skills and employment? Analyze how strong preferences for public jobs shape unemployment, reservation wages, talent bottlenecks, and firm-level hiring constraints—especially among youth, women, and tertiary-educated workers.

    • CONTEXT: In many developing countries, the public sector is a major employer offering higher wages, greater job security, and more predictable benefits than the private sector (World Bank, 2021). When compensation structures are misaligned with market conditions, they can distort workers’ choices: fresh graduates may queue for government jobs, reject private sector offers, raise their reservation wages, or prolong unemployment. Over time, this can contribute to skills shortages in the private sector, especially in high-productivity firms that need skilled workers to grow and compete. World Bank (2023) shows that public-sector queuing disproportionately affects youth, women, and tertiary-educated workers, leading to longer unemployment spells, delayed school- to-work transitions, and erosion of skills during lengthy job search. On the firm side, World Bank Enterprise Surveys consistently report that “inadequately educated workforce” is a top constraint, suggesting a disconnect between worker preferences and labor-market demand.

    • DATA SOURCES:

Category V: Toward New Insights on Job Trends. Harness frontier data (from AI job postings to satellite night-lights) to map real-time transformations in employment. Build tools that help countries anticipate digital opportunities, identify vulnerable workers, and respond to rapidly shifting labor-market needs. Push the boundaries of how we understand jobs in an era of technological disruption.

  • Challenge 8: Demand & supply in digital/AI jobs: understanding supply and demand trends? Build an interactive dashboard showing where digital/AI job demand and supply are rising or lagging across countries, industries, and skill types.

The Development Data Dive 2025 is organized as a collaboration by the WBG Data Talent Board; the Office of the WBG Chief Statistician & Development Data Group; the WBG Data Technology Office; the Prosperity Vice Presidency’s Jobs & Economic Growth Department; the Global Indicators Group (DEC); the Digital Vice Presidency’s Digital Foundations team; the Bureaucracy Lab (DEC); the Department for Outcomes’ Job Council Secretariat; the WBG Youth Summit Secretariat; and the WBG Data Academy.

 

Submissions

Submit team submissions on the Github repository: https://github.com/datacommunitydc/DataDive25

Team submissions:

Submission Guidelines

Your submission will be evaluated across five key dimensions:

Theme (20% of total score)

Present a solution that directly addresses and integrates the challenge theme. Clearly articulate how your analysis and recommendations align with the theme's objectives. Your work should demonstrate a deep understanding of the theme's context and relevance.

Usefulness/Impact (30% of total score)

Focus on developing practical, implementable solutions with measurable impact. Quantify the potential benefits of your solution where possible. Include both immediate and long-term impact assessments, supported by data-driven evidence.

Innovation/Creativity (20% of total score)

Showcase original thinking in your approach to problem-solving. Move beyond conventional analysis methods and demonstrate creative applications of data science techniques. Your solution should offer fresh perspectives or novel combinations of existing approaches.

Technical Rigor & Data Graphics (20% of total score)

Ensure your analysis is thorough and methodologically sound. Include:

  • Clear documentation of your data processing steps

  • Well-designed visualizations that enhance understanding

  • Proper statistical methods and validation

  • Clean, reproducible code

Communication/Storytelling (10% of total score)

Structure your presentation as a compelling narrative that:

  • Opens with a clear problem statement

  • Builds a logical flow from analysis to insights

  • Uses visuals effectively to support your story

  • Concludes with actionable recommendations

Submit your work in a clean, professional format that makes it easy for judges to evaluate each component.