Researcher ORCID Identifier

0009-0004-4679-8043

Graduation Year

2026

Date of Submission

4-2026

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Economics

Reader 1

Professor Darren Filson

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Mehrin S Khan

Abstract

This thesis examines how venture capitalists evaluate early-stage founders by asking, 'To what extent do observable founder signals correlate with reaching a Series A round?' Rather than modelling dynamic progression, I provide a cross-sectional analysis of characteristics associated with Series A outcomes. I develop a Bayesian entrepreneurship framework in which investors update beliefs about founder quality using signals such as elite education, prior exits, and accelerator backing under a macro-dependent cost of capital.

Using 13,820 founders from Harmonic.ai, I estimate LPM, logit, probit, and propensity score matching (PSM) models across six specifications to separate signalling from selection. Several signals are positively associated with Series A outcomes: accelerator affiliation (+15.1 pp), YC backing (+11.6 pp, modelled separately), and prior exits (+8.2 pp). However, the difference between the accelerator and YC coefficients is not consistently statistically significant across specifications. Observable founder characteristics have limited explanatory power: jointly, they account for only 1.7% of variation in Series A outcomes, while cohort-year effects explain over five times as much. The PSM design compares YC-backed founders to similar non-YC founders (including both non-accelerator and other-accelerator participants), reducing the YC estimate to roughly 2–4 pp, consistent with substantial selection effects.

A difference-in-differences design around the March 2022 Federal Reserve rate hikes shows that tighter capital conditions reduced Series A outcomes, with heterogeneous sectoral effects. Overall, founder characteristics function as informative signals rather than purely causal drivers, with most variation driven by unobserved factors such as product-market fit and execution.

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