{
  "title": "AI and the Informal Economy",
  "sourceUrl": "https://www.imf.org/en/publications/fandd/issues/2026/09/point-of-view-ai-and-the-informal-economy-anthony-tan",
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  "summary": "Southeast Asia shows how to extend benefits to millions outside the formal economy",
  "sections": [
    {
      "heading": "Overview",
      "content": "- Focus: How artificial intelligence can benefit people in developing economies by starting from the realities of informal labor markets rather than formal-economy assumptions.\n- Central claim: In Southeast Asia, the informal economy is a structural foundation—flexibility and adaptability are features, not failures—and AI-enabled platforms can strengthen resilience and earnings for informal workers without requiring formalization."
    },
    {
      "heading": "Platform interventions and outcomes",
      "content": "- Grab’s lending and credit assessment:\n  - Last year Grab disbursed $3.2 billion in loans to driver partners and merchant-partner borrowers.\n  - Sixty-eight percent of those borrowers accessed formal credit for the first time through the platform.\n  - The platform uses AI to read platform-generated records of earnings and activity to judge creditworthiness, set credit limits based on platform earnings, deduct repayments in proportion to daily income, and grant hardship relief case-by-case if earnings cease.\n  - Half of driver-partner borrowers say they turned to Grab to avoid unlicensed lenders.\n- Earnings and matching:\n  - Average driver-partner earnings per online hour rose by 5.5 percent year over year last year.\n  - Platform capabilities include real-time demand assessment and job matching to boost earnings and reduce unpaid gaps between jobs; the platform predicts the best spot for the next job.\n- Merchant support:\n  - One in four micro, small, and medium enterprises on the platform now uses Grab’s Merchant AI Assistant.\n  - Merchant AI Assistant supports marketing, menu management, and operations, and underpinned a 15 percent lift in sales."
    },
    {
      "heading": "Skills, training, and AI literacy",
      "content": "- Argument: Realizing AI’s potential requires investment in skills so workers can understand and benefit from AI-enabled tools; informal workers need access to training outside formal-employer structures.\n- Reach of training:\n  - More than 1.5 million driver partners completed at least one GrabAcademy course last year.\n- Interaction of tools and training: AI paired with practical, work-relevant training multiplies gains from platform participation."
    },
    {
      "heading": "Policy recommendations — \"My three asks\"",
      "content": "- First: Fund AI literacy as a public good.\n  - AI literacy should be funded with the same seriousness as roads or energy.\n  - Public-private partnerships can scale permanent, codesigned training structured for workers with irregular hours; example: SkillsFuture Singapore has recognized Grab as an industry leader in championing skills development.\n- Second: Rethink how success is measured.\n  - Governance frameworks should adopt metrics reflecting informal economy dynamics—earnings volatility, participation in multiple platforms, access to credit—rather than calibration around formalization rates or financial stability alone.\n  - Regulators must hold platforms and themselves accountable to those metrics to avoid missing what matters for the informal economy.\n- Third: Unify regional AI governance.\n  - Diversity of regulatory frameworks across Southeast Asia’s main markets constrains platforms’ decisions on data storage and scaling what works.\n  - The May 2026 Association of Southeast Asian Nations’ Digital Economy Framework Agreement establishes a needed regional coordination architecture; policymakers must ensure the framework works for the informal economy and does not impose formal economic assumptions regionally."
    },
    {
      "heading": "Designing policy around informality",
      "content": "- Principle: Effective measures are built around informality rather than imported from systems developed for formal economies.\n- Practical approaches:\n  - Use real-time behavioral data where documentation requirements fail.\n  - Use platform-based distribution where institutional access does not reach.\n  - Build protections that travel with the worker across jobs and platforms (portable coverage).\n- Example of portable social protection:\n  - Malaysia’s March 2026 Gig Workers Act extends social security coverage to gig workers through the national program without making them employees.\n- Wider applicability:\n  - Southeast Asia as a proof of concept for regions where informal work is large; policymakers in Africa, Asia, and Latin America can draw lessons."
    },
    {
      "heading": "Key statistics and regional context",
      "content": "- Informality in Southeast Asia:\n  - 78 percent of the workforce operates in the informal economy.\n- Regional informal employment shares:\n  - Africa: 86 percent of employment.\n  - Asia: 68 percent of employment.\n  - Latin America: 51 percent of employment.\n- Grab program metrics (last year):\n  - $3.2 billion disbursed in loans.\n  - 68 percent first-time formal credit access for borrowers.\n  - 5.5 percent year-over-year rise in average driver-partner earnings per online hour.\n  - More than 1.5 million driver partners completed at least one GrabAcademy course.\n  - One in four micro, small, and medium enterprises uses Merchant AI Assistant; 15 percent lift in sales attributed.\n\nAnthony Tan, \"AI and the Informal Economy,\" F&D Magazine, September 2026.\n\n---\n\n Content in this bundle\n\n- AI and the Informal Economy\n  - AI and the Informal Economy (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - AI and the Informal Economy (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/fandd/issues/2026/09/point-of-view-ai-and-the-informal-economy-anthony-tan"
    }
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    "Authors: ANTHONY TAN",
    "Published: August 18, 2026",
    "Focus: How artificial intelligence can benefit people in developing economies by starting from the realities of informal labor markets rather than formal-economy assumptions.",
    "Central claim: In Southeast Asia, the informal economy is a structural foundation—flexibility and adaptability are features, not failures—and AI-enabled platforms can strengthen resilience and earnings for informal workers without requiring formalization.",
    "Grab’s lending and credit assessment:",
    "Earnings and matching:",
    "Merchant support:",
    "Argument: Realizing AI’s potential requires investment in skills so workers can understand and benefit from AI-enabled tools; informal workers need access to training outside formal-employer structures.",
    "Reach of training:",
    "Interaction of tools and training: AI paired with practical, work-relevant training multiplies gains from platform participation.",
    "First: Fund AI literacy as a public good.",
    "Second: Rethink how success is measured.",
    "Third: Unify regional AI governance.",
    "Principle: Effective measures are built around informality rather than imported from systems developed for formal economies.",
    "Practical approaches:",
    "Example of portable social protection:",
    "Wider applicability:",
    "Informality in Southeast Asia:",
    "Regional informal employment shares:",
    "Grab program metrics (last year):",
    "**AI and the Informal Economy**"
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