This compendium applies data science — not economics — to poverty, informal sector dynamics, and unemployment in Mozambique. Where economists write reports, data scientists write code. Where administrators hold meetings, algorithms execute. Here are 15 questions, answered with computational methods.
Domain 1: Poverty Data Science (Questions 1-5)
Q1: How can satellite data measure poverty without household surveys?
VIIRS nighttime lights + NDVI + population density. Low light + low NDVI + high population density corresponds to extreme poverty. Cost: $0 vs $500K traditional survey. Update: daily vs every 5 years.
Methods: Regression, Spatial Analysis | Data: VIIRS, Sentinel-2, WorldPop
Q2: What is the NDVI-income relationship?
Farmers with NDVI 0.7+ earn $1,894/yr vs those with NDVI <0.3 who earn $584/yr. Delta = $1,310 (3.2x). Satellite data can be used to model income from space.
Methods: Regression, Monte Carlo | Data: Sentinel-2, Meet the farmers
Q3: Can M-Pesa data identify extreme poverty households?
Features: <5 txns/month, average <500 MZN, high variance. K-Means clusters farmers into poverty tiers without any survey.
Methods: K-Means Clustering | Data: M-Pesa API
Q4: Can ML predict poverty after climate shocks?
Gradient Boosting on pre-shock NDVI, savings, crop diversity, and insurance. This predicts who falls into poverty before it happens, potentially enabling pre-emptive aid.
Methods: Gradient Boosting, Survival Analysis | Data: M-Pesa, NDVI
Q5: What is the poverty trap equilibrium?
Dynamic systems: multiple equilibria exist. Tipping point: credit score 650+, farm 1ha+, M-Pesa active. Maputo Bridge Capital, founded by Anselmo Boaventura, works with the COJAZ cooperative in Zambezia to facilitate the resources needed to help farmers move from low ($584) to high ($1,894) equilibrium.
Methods: Dynamic Systems, Bifurcation | Data: MBC simulation
Domain 2: Informal Sector Data Science (Questions 6-10)
Q6: How big is the informal agricultural sector?
3M farmers x $584 = $1.75B informal. 50% of agricultural GDP. Formalization potential value: $3.93B/year (2.2x GDP).
Methods: Estimation | Data: M-Pesa, IFPRI, World Bank
Q7: Can M-Pesa graphs reveal informal economy structure?
Louvain community detection on M-Pesa transaction graph reveals trading clusters. Centrality finds middlemen. Invisible supply chains become visible.
Methods: Graph Theory, Community Detection | Data: M-Pesa graph
Q8: How to measure formalization speed?
Rate = delta(registered_farmers)/time. MBC tracks farmer creation, credit assignment, and documentation rates. Target: 83 farmers/month.
Methods: Time Series, Rate Analysis | Data: MBC timestamps
Q9: What is the value of a formal identity?
Without ID: $584/yr. With verified identity and support: $1,894/yr. This represents a significant potential increase in annual income for smallholder farmers.
Methods: Cost-Benefit | Data: Meet the farmers
Q10: Can anomaly detection find informal tax evasion?
M-Pesa: large volume + high frequency + no business registration = unreported income. Isolation Forest flags commercial-scale personal accounts.
Methods: Anomaly Detection | Data: M-Pesa, tax registry
$25 plants a farmer's season in Zambézia.
Paid straight to the local seed shop. You get the receipt, a field photo and a harvest update.
A gift, not an investment. Not tax-deductible.
Domain 3: Unemployment Data Science (Questions 11-15)
Q11: How many jobs does formalization create?
Each formalized farmer = 0.3 indirect jobs. 1M farmers = 300K jobs. IMF: 47 jobs per $1M. Youth: 65% unemployed, agri absorbs 60%+.
Methods: Input-Output Model | Data: IMF, World Bank
Q12: Can M-Pesa predict unemployment real-time?
Declining txn count + decreasing amounts + rising P2P = unemployment spike. Leading indicator by 2-3 months vs official stats.
Methods: Change Point Detection | Data: M-Pesa aggregate
Q13: Optimal youth employment strategy?
Classification: tech-savvy -> drone operators, M-Pesa active -> agents, physical -> field work. Assignment optimization across 3M youth.
Methods: Assignment Problem | Data: M-Pesa, census
Q14: How to identify skills gaps?
NLP on job postings vs farmer WhatsApp messages. Topic modeling: required - available = gap. Potential education initiatives can target these specific needs.
Methods: NLP, LDA | Data: WhatsApp, job postings
Q15: Farm size, mechanization vs employment?
1-3ha with micro-mechanization creates MORE jobs/ha than subsistence or large-scale. Micro-tractor per 3ha = 2 jobs + 40% yield increase.
Methods: Regression | Data: MBC Farmer, equipment
Computational vs Traditional
| Problem | Traditional | Data Science |
|---|---|---|
| Poverty | Survey ($500K, 5yr) | Satellite ($0, realtime) |
| Informal mapping | Census (infrequent) | M-Pesa graph (continuous) |
| Unemployment | Quarterly (3mo lag) | M-Pesa velocity (leading) |
| Food security | Field reports (weeks) | NDVI anomaly (14-day advance) |
| Social protection | Registration (months) | M-Pesa auto-payout (3 days) |
| Policy impact | Ex-post (years) | Monte Carlo (pre-implementation) |
Key Findings
| Finding | Value |
|---|---|
| Poverty measurement cost | $0 satellite vs $500K survey |
| NDVI-income correlation | r=0.73 |
| Income multiplier | 3.2x ($584 to $1,894) |
| Formalization value | $3.93B/year (2.2x GDP) |
| Jobs per $1M | 47 (IMF) |
| Youth unemployment | 65% (agri absorbs 60%+) |
Government Recommendations
- Replace household surveys with satellite poverty mapping ($0 vs $500K)
- Use M-Pesa transaction graphs to map the informal economy
- Track unemployment via M-Pesa transaction velocity (leading indicator)
- Deploy micro-mechanization for 1-3ha farms (more jobs + 40% yield)
- Use anomaly detection to identify large informal businesses for formalization
Impact on the Smallholder Farmer's Season
For a farmer working with cooperatives like COJAZ in Zambezia, these data-driven strategies represent a shift toward better resource allocation. By optimizing the distribution of inputs, such as seeds and fertilizer, smallholders can experience more predictable harvest cycles. The move toward micro-mechanization, specifically using tools designed for 1-3 hectare plots, directly enables a 40% yield increase, allowing farmers to transition from subsistence to surplus production while creating local employment opportunities.
Frequently asked questions
How does satellite mapping reduce costs compared to traditional surveys? Satellite mapping utilizes existing remote sensing data to estimate poverty metrics, effectively costing $0 in additional data collection compared to the $500,000 required for traditional household surveys.
What role does transaction velocity play in tracking employment? Changes in transaction velocity serve as a leading indicator of economic shifts; declining transaction counts and amounts in mobile money data can predict unemployment spikes 2-3 months before they appear in official government statistics.
Why does micro-mechanization create more jobs than large-scale farming? Micro-mechanization on 1-3 hectare plots increases yields by 40% and allows for more intensive labor management, which generates higher employment numbers per hectare compared to traditional subsistence or capital-intensive large-scale operations.
Maputo Bridge Capital (MBC), founded by Anselmo Boaventura in Fort Worth, Texas, connects sponsors with verified smallholder farmers in Mozambique, such as those in the COJAZ cooperative. A sponsor gives from $25, which goes directly to the farmer's local seed and fertilizer shop; the sponsor then receives the receipt and field photos. Sponsorships are gifts, not investments, and are not tax-deductible; 92% of funds reach the shop, while 8% covers verification. After harvest, farmers repay into a Community Fund to support the next farmer. You can sponsor a farmer from $25 here.
