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 · 社会と人間開発
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 社会保障, education and humanitarian programmes and the effects of disasters and conflict on wellbeing.
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 リスク削減 measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on STEM Commons.
Randomised or quasi-experimental designs compare households that received transfers with similar households that did not, measuring effects on consumption, schooling and wellbeing.
Panel surveys before and after disasters, with comparison areas, estimate effects on income, health and education and how long they last.
Learning programmes are evaluated with comparison groups and validated assessments to estimate effects on foundational skills.
この手法を追跡するためにチームが使う画面:その指標、状態、決定。名前は例であり、値はイメージです。
Society and human development · Impact evaluation for human · Run R-0051
| Main specification | 0.28 | はい |
| Drop early adopters | 0.05 | はい |
| Synthetic control | 0.18 | Weaker |
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 出典の登録簿.
| データ | 役割 | ラベル |
|---|---|---|
| Programme administrative data | Treatment | 公表統計 |
| Panel household surveys | Outcomes over time | 報告値 |
| Learning assessments | 教育 outcomes | 観測 |
二つのストック、圧力と 適応能力という 2 つのストックが、自己強化的な成長ループ、遅れて働く均衡化の反応、圧力が能力を上回ったときの損害で結ばれています。閾値を越えるとシステムは転換しえます。明示された例示値を使う教育用モデルであり、予測ではありません。
管理策
読み取り値
| 年 | 圧力 | 能力 | 知覚された |
|---|---|---|---|
| 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 |
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.
6 本の柱を通じて、Nexus Consortium が主導
Identification assumptions are probed through placebo tests, falsification checks and 感度分析. Replication across settings tests external validity.
| 確認 | 実施方法 |
|---|---|
| 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.
モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。
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.
経済開発 stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
報告書 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
個人データ、重要インフラの機微な詳細、共有する権利のないものは決して送らないでください。寄稿は利用前にレビューされます。送付によって著作者としての権利や編集上の権利が生じることはなく、スポンサーシップで調査結果を買うことは決してできません。
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