Evaluating cash transfers
Randomised or quasi-experimental designs compare households that received transfers with similar households that did not, measuring effects on consumption, schooling and wellbeing.
GCRI · Society and Human Development
Impact evaluation for human development
Causal inference and impact evaluation estimate whether programmes and shocks changed people’s lives, compared with what would otherwise have happened. In this field they evaluate social protection, education and humanitarian programmes and the effects of disasters and conflict on wellbeing.
01Register
Impact evaluation for human development on the record.
Method recordSTEM method
- MethodCausal inference and impact evaluation
- StewardEconomic Development
- Applied inSociety and Human Development
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What causal inference and impact evaluation does here.
Causal inference and impact evaluation develop methods to determine whether and how interventions or exposures cause outcomes. They separate genuine effects from correlation. In society and human development and information and public trust, causal inference shows which risk reduction measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on the STEM Commons.
Effects of disasters on households
Panel surveys before and after disasters, with comparison areas, estimate effects on income, health and education and how long they last.
Education programme evaluation
Learning programmes are evaluated with comparison groups and validated assessments to estimate effects on foundational skills.
03Monitor
Impact evaluation.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Impact evaluation
Society and human development · Impact evaluation for human · Run R-0051
Parallel trends holdGCRI · Society and Human Development
Event study
Method card
- Method
- Causal inference and impact evaluation
- Design
- difference in differences
- Units
- 63
- Label
- MODELLED
Robustness checks
| Main specification | 0.28 | Yes |
| Drop early adopters | 0.05 | Yes |
| Synthetic control | 0.18 | Weaker |
04Data
What the method consumes.
Datasets and records used by causal inference and impact evaluation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Programme administrative data | Treatment | Published statistics |
| Panel household surveys | Outcomes over time | REPORTED |
| Learning assessments | Education outcomes | OBSERVED |
05Simulator
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Society and Human Development in Foundry
- Tested at Society and Human Development in Universe
- Codified in Governance, Security and Society in Standards
- Published in Society and Human Development in Reports
- Taught in Faculty of Health and Society in Academy
06Validation
How results are checked.
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
| Check | How it is done |
|---|---|
| Pre-registration | Evaluation designs and analyses are registered before results |
| Balance and attrition checks | Groups are compared at baseline and attrition is analysed |
| Heterogeneity analysis | Effects are examined by gender, age and other groups |
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
07Methods and evidence
How impact evaluation for human development is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in impact evaluation for human development: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
09Stewardship
Who maintains the method.
Economic Development stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of causal inference and impact evaluation in this field?
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
How are results checked?
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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