Quality Education India Development Impact Bond
Aligned SDGs
- Quality Education India Development Impact Bond
- General overview
- Location
- Involved organisations
- Outcome metrics
- Results
- SyROCCo reports
- Other resources
- Spreadsheet of data
- Quality Education India Development Impact Bond
- General overview
- Location
- Involved organisations
- Outcome metrics
- Results
- SyROCCo reports
- Other resources
- Spreadsheet of data
General overview
Stage of development: Complete
Policy sector: Education
Date outcomes contract signed: Jul 2018
Start date of service provision: Apr 2018
Capital raised (minimum): USD 3m
Max potential outcome payment: USD 9.18m
The investor achieved the targeted return.
Service users: 200k+ individuals
Intervention
The QEI DIB aims to support the education crisis in India by funding four high performing service providers to improve grade appropriate learning outcomes for more than 200,000 primary school aged children over four years. The service providers are KEF, GyanShala, SARD and Pratham Infotech Foundation.The service providers are delivering four interventions with a mix of direct and indirect education model types, including: directly operating classroom (direct), supplementary programmes (direct), and teacher/ school leader training (in-direct).
Target population
Primary school-aged children
Location
Country
- India
Service delivery locations
- Gujarat
- Mumbai
- Uttar Pradesh
- NCR Delhi
Involved organisations
Configuration of contracting parties:
- Framework contract between main parties with individual sub-contracts
Outcome metrics
- Learning outcome improvement 1. Learning outcome improvement: standard deviation (standard points of variation around the mean) as a difference from the comparison group performance. (Directly operating in classrooms)
- Learning outcome improvement 2. Learning outcome improvement: standard deviation (standard points of variation around the mean) as a difference from the comparison group performance. (Remedial programmes)
- Learning outcome improvement 3. Learning outcome improvement: standard deviation (standard points of variation around the mean) as a difference from the comparison group performance. (teacher/principal training)
Powered by SyROCCo SyROCCo reports
The following articles are taken from the Systematic Review of Outcomes Contracts Collaboration (SyROCCo) Machine Learning tool.
The tool is a collaboration between the Government Outcomes Lab and machine learning experts from the University of Warwick, that allows you to navigate and explore data extracted from nearly 2000 academic and grey literature publications related to outcomes-based contracting.
Spreadsheet of data
Important Notice and Disclaimer on INDIGO Data
INDIGO data are shared for research and policy analysis purposes. INDIGO data can be used to support a range of insights, for example, to understand the social outcomes that projects aim to improve, the network of organisations across projects, trends, scales, timelines and summary information. The collaborative system by which we collect, process, and share data is designed to advance data-sharing norms, harmonise data definitions and improve data use. These data are NOT shared for auditing, investment, or legal purposes. Please independently verify any data that you might use in decision making. We provide no guarantees or assurances as to the quality of these data. Data may be inaccurate, incomplete, inconsistent, and/or not current for various reasons: INDIGO is a collaborative and iterative initiative that mostly relies on projects all over the world volunteering to share their data. We have a system for processing information and try to attribute data to named sources, but we do not audit, cross-check, or verify all information provided to us. It takes time and resources to share data, which may not have been included in a project’s budget. Many of the projects are ongoing and timely updates may not be available. Different people may have different interpretations of data items and definitions. Even when data are high quality, interpretation or generalisation to different contexts may not be possible and/or requires additional information and/or expertise. Help us improve our data quality: email us at indigo@bsg.ox.ac.uk if you have data on new projects, changes or performance updates on current projects, clarifications or corrections on our data, and/or confidentiality or sensitivity notices. Please also give input via the INDIGO Data Definitions Improvement Tool and INDIGO Feedback Questionnaire.