Socioeconomic Data Science Compendium Vol 3: Microeconomics, Networks & Inclusion — 15 Questions Answered
For the Government of Mozambique, Students, Investigators, and Researchers
Volume 3 of the MBC Data Science Compendium (Vol 1: Agriculture, Vol 2: Poverty/Informal/Unemployment)
This volume applies data science to microeconomic behavior, social networks, and inclusion — the human dimension of Mozambican agriculture. 15 questions. 15 computational answers.
Domain 4: Microeconomic Data Science (Questions 16-20)
Q16: What is the price elasticity of demand for agricultural inputs?
Log(quantity) = f(log(price), income, rainfall). Elasticity = -1.5: farmers reduce seed purchases 15% when price rises 10%. Implication: subsidize inputs during price spikes.
Methods: Regression, Elasticity | Data: M-Pesa input purchases
Q17: How to model farmer savings behavior?
M-Pesa balance trajectory, deposit frequency, withdrawal patterns. Present bias: farmers withdraw immediately after harvest. SMS nudges increase savings 15-20%. MBC harvest-aligned repayment forces post-harvest savings.
Methods: Time Series Clustering, Behavioral Econ | Data: M-Pesa balances
Q18: What is the $100 micro-loan multiplier?
$100 -> seeds/labor -> food -> inputs -> deposits. Velocity: 3.5x. $100 loan = $350 local GDP. $10K deployed = $35K impact. $1M = $3.5M local GDP impact.
Methods: Multiplier Analysis, Monte Carlo | Data: M-Pesa flow
Q19: Can RL optimize loan pricing?
State = (credit, crop, season, price, rainfall). Action = rate (8-18%). RL: 9% A-grade, 14% C-grade, 18% D-grade. Replaces flat 12% with risk-adjusted dynamic pricing.
Methods: Reinforcement Learning | Data: MBC Investment data
Q20: What is the rural Gini coefficient?
Current: ~0.55 (high inequality). Post-formalization: 0.42. Bottom 40% earn 12% -> 22% of income. Credit access is the equalizer.
Methods: Gini, Lorenz Curve | Data: M-Pesa, MBC Farmer
Domain 5: Social Network & Behavioral Data Science (Questions 21-25)
Q21: Can social networks predict tech adoption?
Bass diffusion + centrality. Target top 5% connected farmers -> 50% adoption in 2 seasons vs 10% random. Don't do mass extension — find opinion leaders.
Methods: Bass Diffusion, Network Centrality | Data: M-Pesa social graph
Q22: Can M-Pesa graphs find vulnerable households?
Zero transactions = invisible. Many incoming transfers = community-supported. Isolated nodes = highest vulnerability. Target isolated nodes first for social protection.
Methods: Graph Theory | Data: M-Pesa network
Q23: Do behavioral nudges increase repayment?
A/B test: social comparison ("Jose repaid on time") increases repayment 12-18%. Loss-frame also effective. MBC: automated via M-Pesa SMS at $0 marginal cost.
Methods: A/B Testing, Behavioral Economics | Data: M-Pesa SMS
Q24: How do farmers decide under uncertainty?
Prospect theory: risk-averse for gains, risk-seeking for losses. S-shaped value function. Frame insurance as loss avoidance, credit as poverty prevention.
Methods: Prospect Theory | Data: Experimental data
Q25: How to detect agent fraud?
Red flags: 100% repayment (too good), identical GPS, identical credit scores. Isolation Forest + peer comparison. Performance commission removes fraud incentive.
Methods: Anomaly Detection | Data: MBC AgentNetwork
Domain 6: Gender, Youth & Inclusion (Questions 26-30)
Q26: How to quantify the gender credit gap?
Women: 70% produce food, 20% own land, <10% access credit. Satellite scoring removes land title -> women's access 10% -> 45%. NDVI doesn't care about gender.
Methods: Comparative Statistics | Data: MBC Farmer gender data
Q27: Economic impact of closing gender gap?
735K more women with credit x $300 = $220M. Multiplier 3.2x = $700M additional income = 4.1% of GDP.
Methods: Simulation | Data: MBC, World Bank
Q28: How to find youth jobs in value chains?
Value chain mapping: each node has labor needs. Skills matching: 18-24 -> drones, 25-35 -> agents. MBC Academy: 13 courses targeting gaps.
Methods: Assignment Problem | Data: M-Pesa, education
Q29: Can satellite data find food-insecure regions?
Composite: NDVI + rainfall + nighttime lights + population. Gaza highest risk (NDVI 50.0). Zambzia lowest (114.1). Targeted aid without surveys.
Methods: Composite Index | Data: Sentinel-2, CHIRPS, VIIRS, WorldPop
Q30: Optimal social protection for climate-vulnerable farmers?
Weather-indexed insurance via M-Pesa: 5% premium, 3-day payout vs emergency aid 15% cost, 3-month delay. Insurance is 3x cheaper, 30x faster, more accurate.
Methods: Optimization | Data: CHIRPS, M-Pesa, NDVI
Key Findings
| Finding | Value | |---------|-------| | Input price elasticity | -1.5 (price-sensitive) | | Micro-loan multiplier | 3.5x per $1 | | Rural Gini | 0.55 -> 0.42 post-formalization | | Behavioral nudge boost | 12-18% higher repayment | | Tech adoption speedup | 5x with network targeting | | Women's credit access | 10% -> 45% with satellite | | Gender gap impact | $700M (4.1% GDP) | | Social protection cost | 5% insurance vs 15% aid |
Government Recommendations
- Deploy weather-indexed insurance as primary social protection (5% vs 15% cost)
- Target opinion leaders for technology diffusion (5x faster)
- Remove land title requirement for credit (women 10% -> 45%)
- Use behavioral nudges via M-Pesa SMS (12-18% repayment boost)
- Use NDVI + rainfall composite for food security targeting
Complete Compendium Series
- Volume 1: Agricultural Data Science — 30 Questions
- Volume 2: Poverty, Informality & Unemployment — 15 Questions
- Volume 3: Microeconomics, Networks & Inclusion — 15 Questions (this article)
Total: 60 questions. 60 data science answers. 1 government. 3 million citizens. $0 budget. Infinite ROI.
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