Regulatory Sandbox Admission and FinTech Firm Survival: Longitudinal Evidence from UK FCA Sandbox Firms: A Secondary-data Archival Study of Firm-level Outcomes and Regulatory Experimentation

Abstract

The current study investigates the longitudinal correlation between the process of admission into FCA Regulatory Sandbox and the subsequent survival of the participating Fin Techs for the period covered (2015-2025). The research relies on the secondary archival data of FCA records and Companies House and builds an un-balanced firm-year panel. The master dataset comprises 219 firm-year observations, covering 25 identified firms from incorporation to 2025. 206 observations determine the risk set of survival because the years following dissolution were excluded. The baseline within-firm treatment-association analysis gives rise to a sample of 172 firm-year observations in 19 firms, with entity-timing quality controls and the need for pre-admission observations. Firm survival is binary, with a value of “1” indicating that a firm is in existence in a given year and “0” indicating that a firm does not exist in a given year. The empirical approach employs a linear probability fixed-effects model with firm and calendar year effects clustered by the firm. The results indicate that the coefficient of the post-admission (β = 0.0457, p = 0.279) is positive, although it is not statistically significant. The age heterogeneity model also yields an insignificant interaction between post-admission exposure and young-firm status (β = −0.0319, p = 0.499). The results for the first 1-2 years after admission (β = 0.0256, p = 0.618) and after 3 or more years (β = −0.0866, p = 0.586) are also non-significant and the direct coefficient-equality test of these periods shows no significant difference (F = 0.34, p = 0.5693). The model with the conditional fixed-effects logistic specification does not converge as there is no variation in surviving within the firms, except for 5 firms. The evidence thus calls for careful description and length of time analysis rather than causality. The paper contributes a transparent firm-year framework for evaluating regulatory experimentation under severe private-firm data constraints.