Introduction and Background

• Ted Miguel: Professor of developmental economics at UC Berkeley
• Founder of Berkeley's Center for Effective Global Action (CEGA) - founded 15 years ago
• Mission: improve research quality informing poverty alleviation and global development work
• Personal motivation: immigrant family background, early exposure to global poverty drove career focus

Open Science Philosophy and Methodology

• Found development economics field lacking data-driven, scientific approach when starting career
• Decisions driven by ideology rather than hard evidence and data
• Committed to bringing best possible data and statistical methods to global poverty debates
• Tools introduced: randomized control trials (RCTs), machine learning, new data sources
• Open science principles from start:
  • Share data openly with other scholars (rare 20 years ago in economics)
  • Publish all results including unexpected/null results
  • Avoid publication bias - publish regardless of donor/government preferences
  • Share results regardless of statistical significance
• Combination of rigorous methods + open science ideals = influential work

Deworming Study - Major Success Case

• First research project, started in grad school with co-author Michael Kremer (Chicago)
• Worked with NGO providing school-based intestinal worm treatment in Kenya
• Context: billions globally have intestinal parasites causing nutritional/health problems
• Treatment: simple pill taken once/twice yearly, costs <$1/year
• Study design: randomized order of scale-up across schools - one of first RCTs in development economics
• Short-term results (few months): kids receiving treatment attended school more, healthier, worms eliminated
• Long-term follow-up (25 years):
  • Kids earn ~10% more in adulthood
  • More likely to move to cities, get more schooling
  • Incredibly high rate of return given $1 treatment cost
• Policy impact: hundreds of millions of kids/year now receive deworming drugs globally
• Programs in India, Nigeria, Pakistan, Ethiopia, Kenya explicitly cite this research
• Data shared publicly for scientific community critique and building

Rural Electrification Study - Null Results Example

• Started 12-13 years ago with Kenya government's major rural electrification effort
• Kenya early adopter compared to neighbors
• Randomized evaluation with Kenya Power company
• Expected transformative impacts on economy, children's schooling (study at night)
• Results: null effects across economic outcomes, agricultural productivity, entrepreneurship, schooling
• Reasons discovered:
  • Median number of appliances was zero
  • Typically only had light bulb, sometimes charged phones
  • Can't afford appliances to use with electricity
• Education findings: kids studied 20 minutes more evening, 20 minutes less afternoon - net zero change in test scores
• Contrast with US 1930s New Deal rural electrification: actively subsidized appliance purchases, documented benefits
• Published null results despite potential disappointment to funders/government

Cash Transfers Study - Broader Impact Measurement

• Collaboration with GiveDirectly NGO in Kenya (2013-2014)
• Designed large-scale study to measure broader spillover effects
• Randomized at higher aggregation levels (sublocation) - certain local economies got lots of cash, others didn't
• Surveyed cash recipients, neighbors, conducted census of local enterprises, measured market prices
• Results:
  • Replicated existing studies showing direct benefits to cash recipients
  • Measured multiplier effects: 2.5x multiplier (every $1 generates $2.50 economic activity)
  • Local businesses benefit: revenue increases, hire more workers including from non-recipient households
  • Minimal price inflation found
  • Long-term health tracking: infant mortality rates fell >1/3 in cash areas
  • Cash + health clinic access = much larger child survival benefits than cash alone

Machine Learning Applications

• CEGA leader in bringing ML tools to development work
• Togo pandemic response example (colleague Josh Blumenstock):
  • Government needed to target cash transfers to poor but lacked census/household data
  • Used ML to predict poverty using cell phone records, remote sensing data
  • Within ~1 month identified several hundred thousand poorest households
  • Government + GiveDirectly targeted cash transfers successfully
  • Approach now adopted in multiple other countries
• Google.org + GiveDirectly hurricane response in Florida:
  • Satellite imagery + ML tools identified neediest areas
  • Cash transfers delivered in days rather than weeks

Intervention Effectiveness Comparisons

• Micro finance: large-scale RCTs found minimal benefits on average
  • Most people aren't great entrepreneurs
  • Small subset start effective businesses, others don't utilize cash well
  • Near zero benefits for large-scale programs on living standards
• One Laptop Per Child: well-studied in large-scale RCTs, found minimal benefits
• Both had significant enthusiasm/hype but research showed limited impact
• Research helps move policy and funding priorities based on evidence

CEGA Organization and Structure

• Based at Berkeley, spans multiple West Coast campuses
• 160+ faculty affiliates, hundreds of grad students
• Partners: Stanford, UCSD, UCLA, University of Washington
• Distinguishing features:
  • Interdisciplinary from start: technologists, computer scientists, climate experts
  • ~2/3 economists, 1/3 other disciplines bringing new technologies/tools
  • Methodological flexibility: early RCT adopters, now ML pioneers
  • Strong commitment to early-stage researcher support
  • Partnerships with institutions in East/West Africa, Bangladesh
  • Inclusive global vision for research impact

Scaling and External Validity Challenges

• Government engagement: launch projects in partnership, include in early discussions
• CEGA maintains relationships with governments, foundations, NGOs for research partnerships
• External validity concerns: results may not transfer across settings
• Deworming example: worked in area with >90% infection rates - wouldn't expect impact where no infections
• Rural electrification: richest 10-15% of villages gained from electricity (bought appliances, started businesses), other 90% didn't
• Need sufficient data and analysis to help governments plan over time
• Equity considerations: some interventions may exacerbate inequality in short run

Research Surprises and Lessons

• Rapid adoption of improved methods in development economics field
• What was unusual 10-15 years ago now commonplace: RCTs, new data sources, ML tools, pre-analysis plans
• Many programs fail - lots of null results in practice
• Sierra Leone governance reform example: World Bank program to weaken traditional chiefs, empower women - found null results across outcomes
• Traditional institutions very persistent
• Need to temper enthusiasm in absence of evidence
• Rigorous methods + commitment to share all results regardless of findings = powerful combination

Funding and Future Research Priorities

• Balance between funding proven interventions vs. research for future discoveries
• Billions in low/middle-income countries across scores of countries still unexplored
• Research investment makes future giving more effective
• Without past research wouldn't know about deworming, cash transfers, bed nets returns
• Pace of learning and evidence generation not slowing down
• Need long time horizons to keep identifying wins for tomorrow, next year, 10+ years from now