Those who travelled on Indian highways till the late ‘90s would recall the quality of their journey, a two-lane highway passing through the middle of the cities and getting stuck in jams. The creation of infrastructure laid completely in the domain of the Government, whose ability to build world-class highways was limited due to lack of funds, amongst other factors. Fast forward to today, India is second to none in giving the best experience to travellers on national highways.

What changed? During those days, the introduction of an innovative BOT contracting model (Build-Operate-Transfer) presented a pathway to not only unlock private capital in the highway sector but also provide inherent financial incentives to contractors to provide a seamless travel experience to citizens. Since then, many such innovative contracting mechanisms have evolved aiming to deliver better experiences for citizens.

Similarly, those who follow the public education space closely would be aware of lower learning levels and the large learning diversity among government school students. One of the promising solutions to tackle these challenges is Personalised Adaptive Learning (PAL), an EdTech solution that can be deployed on existing Information & Communication Technology (ICT) infrastructure in schools. To picture the usage of PAL, assume a student in a class who is proficient in multiplication and another who struggles with basic arithmetic addition. When both these students use PAL, its unique algorithm identifies differing learning needs of these students, and accordingly it provides the personalised learning content to them to close their learning gaps.

However, ensuring the identification of high-quality learning software (like PAL), maintaining ICT infrastructure consistently, keeping schools motivated to use ICT labs, and continually building educators’ capacities present significant operational challenges for administrators. These continued issues limited the success of past ICT implementations in government schools.

In response, NITI Aayog has adopted an innovative contracting model to implement PAL in schools, specifically aiming to address these challenges. This is currently being piloted in the public schools of FOUR aspirational districts of Uttar Pradesh, namely Balrampur, Sonbhadra, Fatehpur, and Chandauli. To improve efficiency, this programme leverages Outcomes-based financing (ObF), or Results-based financing (RbF) to make the contractor duly accountable for delivering learning gains to students. In this model, a significant portion of the PAL contractor’s payment is linked to improvement in the learning levels of students, unlike in the current scenario, where the payment is linked merely to setting up the ICT infrastructure in the schools. The program has linked as much as half of the contractor’s payment to achieving pre-defined improvement in the learning of students over two years. Hence, instead of buying ‘ICT infrastructure’ for public schools, this program aims to buy ‘learning improvements’ for students.

Further, to ensure procurement of quality PAL products, the districts undertook an extensive technical evaluation round amongst the interested PAL vendors. With support from Central Square Foundation (CSF), they used the ‘EdTech Tulna’ framework developed by IIT-Bombay to evaluate and choose the most promising PAL product that will likely deliver maximum learning value for the spend.

Started in April 2023, across 280 schools, such payment linkage is already resulting in driving the desired improvement in service delivery in schools. Usage of PAL across these 280 labs surged past 10 lakh hours by 72,000 students. Undertaking regular teacher training, providing constant support and quick problem resolutions along with conducting weekly visits to schools are some of the few ways the contractor is attempting to meet learning improvement targets. School and district administrations have been also empowered with live usage data and insights to take necessary actions to ensure higher order impact on student learning; hitherto missing in most of earlier such programs.

Indicators from the past have also determined beyond any doubt that hardware infrastructure alone will not lead to better learning outcomes. While we await the final results of the pilot, fundamentally, the model seems to be driving meaningful usage — a key challenge so far seen across states with their current ICT programs. If despite the usage of these PAL labs by students, the desired improvement in students’ learning is not recorded, no outcomes-based payment shall be made to the contractor.

Not only in education, but such evidence-led decision-making combined with the ObF model can also be tried in other sectors. For example, in healthcare, ObF contracting may link contractor’s payments to improved institutional delivery outcomes, as against, let’s say just the installation of delivery-related machinery in hospitals. Similarly, in vocational skilling, the NSDC with its Skill Impact Bond has already linked up to half of the contractor’s payments with job linkages and retention in jobs, as against, merely completion of vocational training programs.

The Outcomes-based payment model has recently got the due attention of the central government. Recognising the potential of ObF contracting, the Hon’ble Finance Minister in her budget speech for FY 2023-24 announced the intent to pilot a few ObF-based programs. Many more ObF-based interventions need to be implemented to see what works and how it can be mainstreamed in government procurements.

To conclude, with limited financial resources available for deployment, combining evidence of what works with a well-designed ObF scheme in government procurement possibly holds the potential to deliver better outcomes to beneficiaries and better returns on taxpayers’ spending.