Chosen theme: Data-Driven Evaluations of Recent Welfare Projects. Explore how rigorous measurement, transparent practices, and lived stories reveal what works, what falters, and how to improve. Subscribe and join our evidence-driven conversation to shape smarter policy.
Designing a Rigorous Evaluation Framework
Begin by naming primary outcomes that truly matter to families—food security, school attendance, clinic visits—then tie them to specific, measurable indicators. Add baselines, realistic targets, and clear definitions to keep everyone aligned under pressure.
Designing a Rigorous Evaluation Framework
When randomization is feasible, randomized controlled trials provide clean comparisons. Otherwise, difference-in-differences, matching, or regression discontinuity designs can estimate impact responsibly. Pre-registration and transparent protocols help prevent cherry-picking and strengthen trust among skeptical stakeholders.
Case Study: Cash Transfers that Surprised Everyone
Setting up the study and baseline
Households were randomly assigned to receive monthly transfers via mobile wallets. Enumerators collected baseline data on spending, stress, nutrition, and schooling. Community leaders co-designed questionnaires, ensuring local relevance and translating measures without losing meaning or cultural nuance.
What the data revealed
Within six months, households reported fewer skipped meals and higher school attendance, while debt reliance declined. Contrary to persistent myths, spending on temptation goods barely moved. Heterogeneity analysis showed stronger gains for female-headed households, guiding refinements in benefit delivery.
A beneficiary’s week, in numbers
Rosa logged three clinic visits for her child, purchased protein twice, and saved the equivalent of five bus fares. Her diary matched transaction records, validating survey claims. Stories like hers give context to charts and calibrate policymaker expectations.
Connecting silos without losing trust
Link social registry records with health, education, and payments data using privacy-preserving identifiers. Standardize formats, map legacy codes, and document every assumption. Data-sharing agreements with clear roles and penalties discourage misuse and keep partners committed to responsible collaboration.
Dashboards that inform real decisions
Good dashboards spotlight action, not decoration. Highlight backlogs, dropout risk, and payment delays with thresholds and weekly trendlines. Include drill-downs to district and case level so supervisors can coach field teams and solve bottlenecks before harm accumulates.
Limits and blind spots to watch
Administrative data often misses informal work, disability status details, or households without phone access. Audit coverage, quantify missingness, and triangulate with surveys. Comment with the blind spots you’ve found and how you patched them without inflating certainty.
Geospatial and Mobile Data for Targeting and Monitoring
Illuminating need with night lights and imagery
Night-time lights and high-resolution imagery can proxy economic activity and infrastructure gaps. Combine them with program maps to detect underserved pockets. Always ground-truth predictions; a bright highway can hide a dark village just beyond the camera’s reach.
Mobility data and service access patterns
Aggregated, anonymized mobility traces reveal travel burdens to clinics and offices. Spikes in travel distance signal eligibility barriers or office closures. Pair patterns with qualitative interviews to understand whether distance, stigma, or information actually deters program enrollment.
Community validation to ground prediction
Hold village meetings to review hotspot maps, gather names of unlisted households, and correct boundaries. Participatory mapping strengthens accuracy and accountability. Tell us how you’ve used community feedback to catch algorithmic mistakes before they steered resources astray.
Translate impacts into understandable units: dollars per household out of extreme poverty, cost per additional school year, or per clinic visit averted. Run sensitivity analyses on key assumptions and publish ranges, not single numbers that imply false precision.
Pilot results rarely scale linearly. Factor in staffing constraints, procurement delays, and political cycles. Track marginal cost as coverage grows and test light-touch variants. Share your favorite scaling pitfalls so others can avoid expensive, avoidable disappointments.
Use waterfall charts, cohort funnels, and uncertainty ribbons that even busy cabinet members can digest quickly. Pair each figure with one takeaway sentence and one next action. If a chart lacks an action, it probably belongs in an appendix.
Equity at the Center of Every Analysis
Disaggregated results that matter
Break results by gender, age, disability, location, and income quintile. Track who benefits first, last, or never. Publish accessible summaries so affected groups can challenge conclusions and co-design fixes where gaps persist or worsen over time.
Fairness checks for algorithms and humans
Eligibility models and caseworker decisions both carry bias. Test false positive and false negative rates across groups, then adjust thresholds or rules. Document trade-offs transparently so the public understands why certain fairness choices were ultimately made.
Co-creating interpretations with communities
Host feedback circles where residents react to findings, flag anomalies, and propose changes. Translate results into local languages and formats. Community ownership turns evaluation from audit to partnership, sustaining accountability long after the field team packs up.
