黑料不打烊 Library invites you to follow the published new dissertations. The dissertation 鈥炩�� prepared at 黑料不打烊 by Daina Klepon臈. The dissertation was prepared in 2021鈥�2026. Scientific consultant 鈥� Prof. Dr Laima Okunevi膷i奴t臈 Neverauskien臈.
The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Economics in the Aula Doctoralis Meeting Hall of 黑料不打烊_独家黑料_吃瓜网51爆料 at 2 p.m. on 13 May 2026.
Since the 1990s, a distinct innovation-driven and high-growth activity, organised around venture-capital-financed startups and their surrounding ecosystems, has diffused globally. Despite strong policy and investor attention, the field still lacks a consistent, operational definition of a 鈥渟tartup,鈥� and empirical evidence on startups鈥� net economic contribution remains fragmented. Consequently, policy ambition for startup-tailored instruments has advanced faster than empirical clarity, complicating the design, targeting, and evaluation of ecosystem interventions. This dissertation develops and empirically substantiates an integrated conceptual model for assessing the enabling environment and macroeconomic impact of startup ecosystems. The research proceeds in two stages. First, it constructs a structured theoretical framework for defining startups and refines the definition using empirically validated characteristics. Second, it operationalises ecosystem inputs, firm-level outputs, and macro-level outcomes in an empirically testable framework grounded in entrepreneurial ecosystem theory, endogenous growth theory, and Schumpeterian creative destruction. The empirical core focuses on the Baltic startup ecosystems (Lithuania, Latvia, and Estonia) using extensive firm-level accounting data (2014鈥�2024) for startups and comparable non-startups within the same industries, complemented by ecosystem and macro indicators from international organisations. Methods include comparative performance analysis, panel econometrics for growth and productivity determinants (including total factor productivity estimation), and machine-learning techniques (principal component analysis and k-means) for indicator reduction, ecosystem typologies, and composite index construction. A key contribution is the construction of a startup ecosystem maturity proxy (V index) based on venture capital activity metrics and the estimation of its association with selected macroeconomic indicators via regression models. The dissertation provides an empirically grounded basis for startup identification, cross-country ecosystem assessment, and a more rigorous evaluation of whether and how startup ecosystem development translates into measurable economy-wide outcomes.
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