Cross-Country Determinants of Early-Stage Necessity and Opportunity-Motivated Entrepreneurship: Accounting for Model Uncertainty

Model uncertainty is one of the most pervasive challenges in the social sciences. Cross-country studies in entrepreneurship have largely ignored this issue. In this paper, we evaluate the robustness of 44 possible determinants of early-stage opportunity-motivated entrepreneurship (OME) and necessity...

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Veröffentlicht in:Journal of small business management 2018-03, Vol.56 (S1), p.243-280
Hauptverfasser: Nikolaev, Boris N., Boudreaux, Christopher J., Palich, Leslie
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creator Nikolaev, Boris N.
Boudreaux, Christopher J.
Palich, Leslie
description Model uncertainty is one of the most pervasive challenges in the social sciences. Cross-country studies in entrepreneurship have largely ignored this issue. In this paper, we evaluate the robustness of 44 possible determinants of early-stage opportunity-motivated entrepreneurship (OME) and necessity-motivated entrepreneurship (NME) that are broadly classified in four groups: (1) economic variables, (2) formal institutions, (3) cultural values, and (4) legal origins and geography. The results, which are based on a representative world sample of up to 73 countries, suggest that institutional variables associated with the principles of economic freedom are most robustly correlated with OME and NME. Our findings also identify net income inequality and Scandinavian legal origins as weakly robust predictors of both types of entrepreneurial activity. Furthermore, we find that log GDP per capita is only a weakly robust predictor of NME, but not OME. We discuss implications for future research.
doi_str_mv 10.1111/jsbm.12400
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source Wiley Online Library - AutoHoldings Journals; EBSCOhost Business Source Complete
subjects Cultural values
Economic models
Entrepreneurs
Entrepreneurship
Geography
Income inequality
Robustness
Social sciences
Uncertainty
title Cross-Country Determinants of Early-Stage Necessity and Opportunity-Motivated Entrepreneurship: Accounting for Model Uncertainty
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