Pedigree Bias in
Indian Hiring
The Hidden Cost of College-Based Filtering
Pages
15
Audience
HR & Leadership
Domain
Results & Research
Published
2025
Abstract
The Indian tech hiring market filters heavily on college pedigree. “IIT/NIT only” policies — explicit or implicit — exclude over 99% of engineering graduates before any evaluation of actual capability. This paper examines the evidence on what pedigree actually predicts, quantifies the cost of pedigree-based filtering, and presents data from LayersRank deployments showing how capability-based assessment identifies strong talent that pedigree filtering misses.
Executive Summary
Key Findings
IIT/NIT graduates represent less than 1% of Indian engineering talent.
Filtering to this pool excludes 99%+ of candidates before any evaluation.
Pedigree correlates with job performance, but weakly.
The correlation fades significantly after 2–3 years of work experience. For experienced hires, college brand has minimal predictive value.
Pedigree filtering has significant hidden costs.
Higher salary expectations, reduced diversity, smaller talent pools, and missed hires from non-target schools.
Capability-based assessment identifies strong non-pedigree talent.
In LayersRank deployments, 30–40% of top-scoring candidates come from colleges that would fail traditional pedigree filters.
Companies can maintain quality standards while expanding pools.
The bar doesn’t need to lower — it needs to measure the right things.
Recommendations
Remove or de-emphasize college filters in first-round screening
Evaluate all candidates on demonstrated capability
Use structured assessment to ensure consistent standards
Track outcomes by college tier to validate approach
The Pedigree Landscape in India
2.1 The Numbers
India produces approximately 1.5 million engineering graduates annually from:
| Tier | Institutions | Count | Annual Grads | % Total |
|---|---|---|---|---|
| Tier 1 | IITs | 23 | ~16,000 | 1.1% |
| Tier 1 | NITs | 31 | ~20,000 | 1.3% |
| Tier 1 | BITS, IIIT, top private | ~20 | ~15,000 | 1.0% |
| Tier 2 | State colleges, mid-private | ~500 | ~200,000 | 13.3% |
| Tier 3 | Regional colleges | ~3,000+ | ~1,250,000 | 83.3% |
Total Tier 1: ~51,000 graduates (3.4%)
Non-Tier-1: ~1,450,000 (96.6%)
When companies filter to “IIT/NIT only,” they’re choosing from 2.4% of the talent pool.
2.2 How Pedigree Filtering Works
Pedigree filtering operates at multiple stages:
Explicit filtering
ATS rules that reject applications from non-target colleges. Job postings that specify “IIT/NIT preferred.”
Implicit filtering
Recruiters sorting resumes by college first. Interview panels unconsciously favoring candidates from familiar schools.
Network effects
Referral programs that over-represent current employee demographics. Campus recruiting that only visits target schools.
Resume ordering
Candidates from target schools getting more attention, faster responses, and benefit of the doubt in marginal cases.
2.3 Why Companies Filter on Pedigree
“Reviewing 100,000 applications is impossible. College acts as a pre-filter.”
“IIT admission is competitive. Competitive admission predicts capability.”
“We know IIT graduates. They’re a known quantity.”
“Our IIT employees refer other IIT candidates. The network is self-reinforcing.”
Each argument has validity — and significant flaws, which we examine in Section 3.
What Pedigree Actually Predicts
3.1 The Research on College and Job Performance
Meta-analyses of education credentials and job performance show:
Education level → job performance
Schmidt & Hunter, 1998
College grades → job performance
Roth et al., 1996
College prestige → job performance
various studies
For context:
0.09–0.12
College prestige
~1% of variance explained
0.38–0.51
Structured interviews
Industry standard
0.54
Work sample tests
Best single predictor
College prestige is among the weakest predictors of job performance that companies commonly use.
3.2 What IIT Admission Actually Measures
JEE measures
- Performance on physics, chemistry, mathematics problems
- Timed problem-solving under pressure
- Pattern recognition and analytical reasoning
- Recall of concepts taught in coaching programs
JEE does NOT measure
- Communication skills
- Collaboration ability
- Learning velocity in novel domains
- Persistence on ambiguous problems
- Domain expertise (acquired after college)
- Work ethic and professionalism
3.3 The Fading Pedigree Effect
The predictive value of college pedigree decreases with experience:
| Experience Level | Predictive Value |
|---|---|
| Campus hire (0 years) | Moderate |
| 1–2 years | Low-Moderate |
| 3–5 years | Low |
| 5+ years | Minimal |
After 3–5 years of work experience, actual job performance, skills acquired, and demonstrated results dwarf any signal from college brand. For experienced hiring, pedigree filtering makes almost no sense from a predictive standpoint.
3.4 What Pedigree Actually Proxies
When college pedigree does correlate with outcomes, it often proxies other factors:
Socioeconomic status
JEE coaching costs ₹2–5 lakh. Students from wealthy families have access; others don’t. “IIT caliber” partly means “could afford coaching.”
Urban advantage
Coaching centers concentrate in metro areas. Rural students face structural disadvantage regardless of raw ability.
Family education
First-generation college students are underrepresented at IITs. Parental education provides advantages unrelated to candidate ability.
Early decision-making
The choice to pursue IIT happens at age 14–15. Many talented people made different choices as teenagers.
Filtering on IIT partly filters on wealth, geography, and family background — not just capability.
The Cost of Pedigree Filtering
4.1 Direct Costs
Salary premium
IIT graduates command 20–40% higher salaries than equivalent non-IIT candidates. For a team of 50, this costs ₹50–100 lakh annually.
Smaller talent pool
Competition for 50,000 Tier-1 graduates is intense. Every company wants the same candidates. You pay more and get fewer.
Longer time-to-fill
Restrictive filters extend searches. Positions stay open longer. Opportunity cost accumulates.
Reduced offer acceptance
Top IIT candidates have many offers. Your acceptance rate may be 40–50%. You must interview more candidates to fill each role.
4.2 Indirect Costs
4.3 The Opportunity Cost Calculation
Consider a company that needs 100 engineers:
Pedigree Approach
Need to repeat cycle or compromise
Capability Approach
Roles filled faster, at lower cost
Alternative Approaches
5.1 Capability-Based Hiring
Instead of filtering on credentials, evaluate demonstrated capability:
What to measure
- Technical skills relevant to the role
- Problem-solving approach
- Communication clarity
- Learning orientation
- Collaboration patterns
How to measure
- Structured assessments with consistent criteria
- Work samples or simulations
- Behavioral interviews with defined rubrics
- Reference checks focused on relevant competencies
5.2 Blinding and Anonymization
Remove pedigree signals from evaluation:
This forces evaluation on substance rather than credential.
5.3 Outcome Tracking
If Tier-2 hires perform comparably to Tier-1 hires, expand Tier-2 sourcing.
LayersRank Data: Beyond Pedigree
6.1 Methodology
12,847
Candidate assessments
identity-blind evaluation
23
Companies
using LayersRank
1,247
Post-hire tracked
6+ months performance data
6.2 Assessment Scores by College Tier
Distribution of LayersRank scores by college tier:
| College Tier | Mean Score | Std Dev | Top 20% Rate |
|---|---|---|---|
| Tier 1 (IIT/NIT/BITS) | 72.4 | 14.2 | 28% |
| Tier 2 (State/Mid-Private) | 64.8 | 16.1 | 18% |
| Tier 3 (Regional) | 58.3 | 17.8 | 12% |
Interpretation
Tier-1 candidates score higher on average, but the distributions overlap substantially. 18% of Tier-2 and 12% of Tier-3 candidates score in the top 20% overall — these are candidates who would be filtered out by pedigree but are genuinely strong.
6.3 High Scorers by College Tier
Where do top-20% candidates come from?
| Source | % of Top 20% | % of Pool |
|---|---|---|
| Tier 1 | 42% | 31% |
| Tier 2 | 38% | 42% |
| Tier 3 | 20% | 27% |
Key Finding
58% of top-scoring candidates come from Tier-2 and Tier-3 colleges.
If you filter to Tier-1 only, you miss more than half of your strongest candidates.
6.4 Post-Hire Performance Correlation
For candidates with 6+ months of performance data (n=1,247):
| Metric | LayersRank Score | College Tier |
|---|---|---|
| Manager rating (1–5) | r = 0.42 | r = 0.11 |
| Promotion within 18 months | r = 0.38 | r = 0.08 |
| Retention at 12 months | r = 0.29 | r = 0.05 |
LayersRank assessment scores predict job performance 3–4x better than college tier.
6.5 Case Example: GCC Engineering Team
A Fortune 500 GCC shifted from pedigree-based to capability-based hiring:
Before (Tier-1 only)
After (All tiers, LayersRank)
Performance improved slightly while expanding the talent pool 4x and reducing salary costs by ~15% (lower Tier-1 premium).
Implementation Recommendations
7.1 For Campus Hiring
7.2 For Experienced Hiring
7.3 For Organizational Change
Start with a pilot
Run capability-based assessment alongside traditional process. Compare outcomes.
Build data
Track which assessment approach produces better hires. Let data drive decisions.
Communicate the change
Help hiring managers understand that capability-based assessment maintains (or improves) quality while expanding access.
Set targets
Consider diversity targets for college tier representation. Hold teams accountable.
7.4 For Individual Hiring Managers
Conclusion
Pedigree filtering is the default in Indian tech hiring. It’s comfortable, defensible, and wrong.
The evidence is clear:
College prestige weakly predicts job performance
The correlation is among the lowest of any commonly used hiring signal.
Pedigree filtering excludes most of the talent pool
96.6% of engineering graduates never get evaluated.
Strong candidates exist across all college tiers
58% of top scorers come from Tier-2 and Tier-3 colleges.
Capability-based assessment identifies them
LayersRank scores predict performance 3–4x better than college tier.
The question is not “Should we lower our bar for non-Tier-1 candidates?”
The right question:
“Should we measure the right things for all candidates?”
Same bar. Better measurement. Wider pool.
Companies that make this shift gain access to talent their competitors ignore. They build more diverse teams. They often reduce costs while maintaining or improving quality.
The pedigree premium is a tax you don’t have to pay.
References
- 1.
Roth, P. L., BeVier, C. A., Switzer, F. S., & Schippmann, J. S. (1996). Meta-analyzing the relationship between grades and job performance. Journal of Applied Psychology, 81(5), 548–556.
- 2.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274.
- 3.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all?. Journal of Personality and Social Psychology, 86(1), 148–161.
- 4.
AICTE (2023). All India Survey on Higher Education. Ministry of Education, Government of India.
- 5.
National Institutional Ranking Framework (2024). Engineering Rankings. Ministry of Education, Government of India.
For questions about this research or to discuss capability-based assessment for your organization, contact info@the-algo.com
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