Evaluating a credit line or guarantee
Borrowing firms are compared with similar firms outside the programme using matching, discontinuities in eligibility rules or staggered roll-out, to separate the programme's effect from selection.
GRA · Development Finance Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Impact evaluation of development finance
Causal inference and impact evaluation test whether development finance changed outcomes, and by how much, compared with what would otherwise have happened. They underpin additionality claims, results reporting and the evidence base that later appraisals draw on.
01Register
Impact evaluation of development finance on the record.
Method recordSTEM method
- MethodCausal inference and impact evaluation
- StewardEconomic Development
- Applied inDevelopment Finance Nexus
- Data4 inputs
- Checks4 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 development finance, critical infrastructure and economic development, 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.
Infrastructure impact on households
Difference-in-differences designs compare areas reached by a road, grid extension or water scheme with comparable areas before and after, with spillovers to neighbouring areas tested.
Designing evaluation into a project
Evaluation design, baseline data and comparison groups are set during preparation, when roll-out can still be phased or randomised, instead of reconstructed after completion.
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
Development finance · Impact evaluation of development · Run R-0044
Parallel trends holdGRA · Development Finance Nexus
Event study
Method card
- Method
- Causal inference and impact evaluation
- Design
- difference in differences
- Units
- 87
- Label
- MODELLED
Robustness checks
| Main specification | 0.06 | Yes |
| Drop early adopters | 0.13 | Yes |
| Synthetic control | 0.19 | 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 |
|---|---|---|
| Project monitoring and results data | Treatment status and outputs | REPORTED |
| Household and firm surveys | Outcomes and covariates | ESTIMATED |
| Administrative records | Outcomes at scale, such as tax or credit records | Published statistics |
| Satellite night lights and land cover | Outcome proxies where surveys are absent | MODELLED |
05Scenario model
Policy paths and what they do to the risk.
How three families of instruments change a risk outcome over time with adoption lags, coverage and compliance, the policy scenarios behind transition and physical risk assessments. Scenario: named assumptions about the future, not a forecast.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- 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
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Development Finance Nexus in Foundry
- Tested at Development Finance Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Development Finance Nexus in Reports
- Taught in Faculty of Finance and Economics 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 |
|---|---|
| Placebo and pre-trend tests | Comparison groups are checked for parallel trends before the intervention and for no effect where none is expected |
| Sensitivity to unobserved confounding | Estimates report how strong a hidden confounder would need to be to overturn them |
| Spillover tests | Effects on nearby non-participants are estimated so that they do not bias the comparison |
| External validity statement | Each estimate states the population and context it applies to |
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
07Exposure, loss and capital
What impact evaluation of development finance means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in impact evaluation of development finance reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
08Connections
Topics that rely on this method.
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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