Why are new and baby IIMs rejecting candidates with high CAT percentiles and strong profiles?

The apparent paradox here is real, and we've seen it come up repeatedly in student conversations: a candidate with a 99.1 percentile CAT score, strong academics across the board — say, a 9/8/9 profile — and a conversion at XLRI gets rejected outright by newer IIMs like Sambalpur or Udaipur. It's genuinely confusing, and the frustration is understandable. But there are a few coherent theories worth walking through.

The first is strategic filtering. Newer IIMs may be deliberately rejecting candidates they perceive as overqualified — not out of oversight, but because they assume such candidates will convert to better colleges and won't join anyway. From an institutional standpoint, this reduces dead weight on their waitlist and improves their yield numbers. It sounds counterintuitive, but it's a rational admission strategy if the goal is offer acceptance rate over applicant quality signaling.

The second theory has more structural backing. Several newer IIMs appear to place heavy weight on work experience and specific profile types in their evaluation framework — with 30% weightage reportedly assigned to diversity and work experience. The "prescribed profile" that seems to convert well at these institutes tends to be female, non-tech, non-management backgrounds with strong academics. It's not purely about the CAT score or even the overall percentile.

The third theory is what we'd call an upper ceiling effect — where candidates scoring above a certain threshold actually fall outside the college's target profile range. This isn't merit blindness; it's a deliberate calibration to attract students who are genuinely likely to join.

One perspective we've encountered from experienced professionals in the admissions space draws an analogy to Veblen goods — the idea that exclusivity and perceived scarcity increase desirability. Under this lens, some newer IIMs may be maintaining deliberate rejection rates to preserve brand positioning, even at the cost of rejecting strong candidates.

That said, there's genuine disagreement on how much diversity markers actually help in practice. Even candidates who fit the so-called prescribed profile — female, non-engineer, strong academics — have reported rejections, which suggests the criteria aren't entirely transparent or consistently applied. The picture that emerges is one of meaningful unpredictability in the process, with outcomes feeling luck-based to many applicants despite the IIMs' published weighted evaluation frameworks.

From what we've tracked, there's also a broader question worth raising: if you've already converted XLRI or MDI, does a baby IIM rejection actually matter? In our experience, the honest answer is usually no — there's no meaningful monetary or career benefit to chasing an additional admit from a newer IIM when you're already holding a strong offer. The psychological sting is real, but the strategic calculus doesn't hold up.

The clearest takeaway from all of this is that newer IIMs are operating on criteria that extend well beyond published metrics — prioritizing work experience, specific demographic combinations, and a realistic assessment of whether a candidate will actually join. For applicants trying to plan around this, the practical advice is to not over-index on CAT percentile as a predictor of baby IIM outcomes, and to treat those results as genuinely separate from the broader merit conversation.