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From Past Ventures to Present Success: Does Human Capital Drive Performance in Entrepreneurship?

Author

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  • Rai, Sabhya

Abstract

Accumulated entrepreneurial experience, as a form of human capital, plays a critical role in shaping current venture revenue. Building on insights from entrepreneurship and human capital theory, the study establishes a causal inverted U-shaped relationship between prior entrepreneurial experience and current revenue. By providing an observable measure of financial performance, the analysis helps investors identify ventures whose co-founders fall within an optimal experience range to maximize financial and strategic returns, and guides entrepreneurs in assembling founding teams with experience profiles that enhance revenue and investment prospects. To address endogeneity, it employs Caetano et al.'s (2023) control function approach, leveraging 'bunching' at zero average venture experience to isolate the effect from potential confounders. The findings reveal that in ventures with three co-founders (the most common team size), each additional prior venture increases current revenue by approximately 10%, up to a combined average of 13 prior ventures across the founding team. Beyond this threshold, additional experience yields diminishing returns on performance.

Suggested Citation

  • Rai, Sabhya, 2026. "From Past Ventures to Present Success: Does Human Capital Drive Performance in Entrepreneurship?," GLO Discussion Paper Series 1711, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:1711
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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