Fresh Revelations: A Year Later

Flashback: “Revisiting history with alacrity, dispassionate critique, and foresight, this article ventilates the key issues and parameters associated with the newly proposed #university #funding #model in Kenya. Will the radical shift introduced by President William Ruto help reform universities to better deliver on #SDG4? I believe the new model will score resoundingly, but it is destined to do so only at either of the two extremes of the scale.” This excerpt from an earlier article published in the IBD Blog on 31st May 2023 lauded the government’s commitment to a radical, student-centred funding formula, designed to revolutionise the landscape of university financing in line with SDG4 (Interrogating Kenya’s New Public University Funding Model: Towards a Needs-Based and Student-Centred Allocation Formula – IBD impactborderlessdigital.com)). More than a year later, it’s clear that the model is tipping towards failure—and the consequences are dire.

When Kenya unveiled its new public university funding model, many hoped for a fairer, needs-based approach that placed students at the heart of the financial equation. Fast forward a year, and the model is faltering, raising concerns that the promises made by the government may have been more about rhetoric than reform. Has the shift truly delivered on its promise, or is it another case of lofty expectations crushed under the weight of a flawed system?

The Erstwhile Sweet Promise

The new model, heralded as the beacon of equity in education, proposed a shift to a needs-based, student-centred funding formula. This meant that financial support be responsive to a student’s genuine financial needs and would also follow the student, not the institution, hence fostering competition among universities to attract students. The universities themselves could democratically declare programme costs, subject to a maximum cap specified by the maximum Differentiated Unit Cost (DUC).

However, what appeared to be a well-intentioned reform has now become a source of frustration and confusion. The promise was that declared programme costs would offer an all-inclusive education package. But with the government falling short on its funding obligations, the dream of affordable education is quickly dissipating, especially for the most vulnerable households.

To refresh our memory, below is a summary of the fundamentals of the Differentiated Unit Cost (DUC) model that informed the new programme costing at public universities.

In a typical academic year, the baseline the Differentiated Unit Cost (DUC) model assumed from a cross-country benchmarking exercise amounted to 1,800 hours (refer to the European Credit Transfer System). The contact hours with a professor (say lecturers, whose average per capita pay in one academic year is F) was fixed at 40%, leaving 60% for self-study by the student. In Kenya’s case, it was observed that most STEM courses are allocated more hours than this standard reference, with 25% over and above the regular programme hours, R, reserved for industry-based learning. With 1,800 hours as the benchmark, the student workload factor, k, becomes 1.25R/(0.4×1800). For clinical and engineering studies, k is much higher than unity (1). Their student/lecturer ratio, r, is also lower than for the others, so as to ensure quality training with the instruments and laboratory facilities available.

Interrogating the Means Testing Instrument

The Means Testing Instrument (MTI) now raises more questions than answers as to whether it has sufficient historical database per student as once highly publicised or it is the handlers who are challenged in data literacy. Why are we still landing learners in the wrong bands?  It is utterly disheartening to hear explanations from decision makers on why students have fallen into the wrong bands – betraying a sheer lack of understanding or responsibility as public servants, if not both.

Henceforth, probing questions arise, mainly from inquisitive modellers and systems thinkers:  Do the decision makers understand that the decision variables should not carry equal weight, since some of them, the schools attended for example, are less important than others? Are the intervening variables made distinct from moderating variables? Are they applying static thinking as opposed to dynamic thinking? Static thinking focuses on events or equilibrium points, hence menial references to the schools the learners attended as opposed to the level of sacrifice, dependency ratios, or the number of helping hands that supported the students to attend such schools. We need dynamic thinking to perceive the overall trajectory of the variables that create a dynamic equilibrium, such as the spread of learners across schools and the chances or deliberate choices that landed them where they learnt.

As a cornerstone tool of the new funding model was the much-touted Means Testing Instrument (MTI). The MTI was meant to evaluate each student’s financial situation and allocate resources accordingly. In theory, this would ensure that students from less privileged backgrounds received the highest levels of support, while those with more means would shoulder a greater share of the costs.

However, the reality has been far from perfect. The MTI has proven unreliable, placing students into incorrect financial bands. As a result, many deserving students are being unfairly penalised, while others receive more support than they should. This situation mirrors the infamous early days of the Higher Education Loans Board (HELB), where students had to appeal through priests and Deans of Students to get the support they deserved. It appears that history is repeating itself, with vulnerable students paying the price. A true case is where a student received a loan of KES 40,000 with no bursary (grant) for the first academic year at the University of Nairobi. After being orphaned at the end of the first academic year and applying for the full loan of KES 42,000 and bursary for the second academic year, complete with a copy of the father’s death certificate to prove he was more needy, he got punished with an allocation of KES 25,000 and no bursary for second year!

The government earlier exuded confidence that the MTI would be capable of deducing a student’s level of need based on various factors, such as household income, the nature of previous schools attended, the cost of the programme a student is going to take, and gender, among others. The MTI suggested that approximately 29% of the total student population in Kenya falls under the ‘vulnerable and extremely needy’ category. Additionally, 17% are classified as ‘needy’, while the remaining 54% are categorised as ‘less needy’. The neediest student category was originally supposed to receive 100% government scholarship with upkeep allowance as the least needy category got allocated a higher share of loans (55%) with less in government scholarship (38%), and 7% to be met through household contribution.

Re-adjustment: A Step Backward

A year into this model, the situation has only worsened. The funding bands have been adjusted, but not for the better. Has the aspect of geographical context received due spatial justice, noting that a monthly household income of KES 120,000 in Nairobi is not comparable to the same somewhere in a rural setting? Again, is assigning a monthly household income of more than KES 120,000 to Band 5 sensitive to the large variation of incomes above that, which classifies a monthly income of even a million KES as a member of the same set. To illustrate the point, students categorised in Band 1, with household incomes of up to KES 5,995, are now expected to contribute 5% of their education costs, while receiving 70% in government scholarships and 25% in loans. For those in Band 5, with household monthly incomes above KES 120,000, the burden is even greater, with households expected to cover 40% of the costs with no upkeep allowance for the students. Even Band 4 categories (household monthly income up to KES 120,000) will not receive any upkeep allowance as the household contributes 30% of the programme costs.

The detailed allocation by Band

Band 1 (monthly household income up to KES 5,995) should receive 70% government scholarship, 25% loan, and KES 60,000 upkeep allowance, and the household has to contribute 5%.

Band 2 (monthly household income up to KES 23,670), the respective figures are 60%, 30%, KES 55,000, and 10% household contribution.

Band 3 (monthly household income up to KES 70,000), the respective figures are 50%, 30%, KES 50,000, and 20% household contribution.

Band 4 (monthly household income up to KES 120,000), the respective figures are 40%, 30%, nil upkeep, and 30% household contribution.

Band 5 (monthly household income above KES 120,000), the respective figures are 30%, 30%, nil upkeep, and 40% household contribution.

These changes have placed a disproportionate burden on students and their families, who had been assured that the MTI would offer a fair assessment. Now, instead of receiving the promised 100% scholarship, many students will find themselves struggling to pay the fees, with reduced government support.

The Government’s Data Problem

At the heart of the issue lies a critical flaw in the government’s data infrastructure. The MTI was meant to rely on decades of data to accurately assess student needs. That makes for a time long enough and data-rich enough for model calibration, followed by sensitivity analysis, verification, validation, and evaluation. Yet, it appears that much of this data is either missing or unreliable. It seems students are now being assessed almost entirely based on the information they provide on their application forms, rather than a robust, data-driven evaluation process.

The MTI is supposed to be a dynamic instrument, capable of weighing variables such as household income, dependency ratios, and the sacrifices made by families to send students to school. But instead, it operates like a static machine, focused on simplistic and irrelevant metrics, such as the name of the school a student attended, without considering the full context of their financial situation. The assignment of the weights must be rational with active multi-stakeholder engagement, a weak point that has defined the boardroom policy-making processes in Kenya, throwing mere tokenism or lacklustre consultation unto the masses.

Final Thoughts: A Call for Accountability

The government needs to come clean on the state of the MTI. If the data it promised to use has not been gathered or applied effectively, then it is time for heads to roll. The system is clearly broken, and it is Kenya’s youth who are suffering the consequences.

We cannot afford another year of broken promises and mismanagement. Our students deserve a fair, transparent funding model that delivers on its promise of equity and access. The time for excuses is over. Kenya’s future depends on getting this right.

By Nashon Adero

# IBD Series