Studies on the influence of entrepreneurial role models (peers) on the decision to start a firm argue that entrepreneurial role models in the local environment (1) provide opportunities to learn about entrepreneurial tasks and capabilities, and (2) signal that entrepreneurship is a favorable career option thereby reducing uncertainty that potential entrepreneurs face. However, these studies remain silent about the role of institutional context for these mechanisms. Applying an extended sender–receiver model, we hypothesize that observing entrepreneurs reduces fear of failure in others in environments where approval of entrepreneurship is high, while this effect is significantly weaker in low-approval environments. Taking advantage of the natural experiment from recent German history and using data from the Global Entrepreneurship Monitor Project, we find considerable support for our hypotheses.
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The decoding of information in terms of understanding the demonstrated entrepreneurial activity is an additional technical aspect.
We assume that entrepreneurs send, on average, the same signals regardless of the underlying institutional approval. Relaxing this assumption should not be critical to our framework as long as differences in the kind of signals sent are due to differences in approval of entrepreneurship. In this respect, entrepreneurs in low-approval countries might share different signals or knowledge since they feel particularly conditioned and judged by their environment compared to high-approval countries. This mechanism would be in line with our reasoning.
Socialism also imprinted regional development. Fritsch (2004) illustrates that East and West Germany are marked by distinct regional growth regimes. Hence, the conditions for regional growth and entrepreneurship should be different (see also, Fritsch et al. 2014). Transition-specific adjustment processes might explain why East German regions have significantly lower economic capabilities and relatively more unfavorable long-term prospects than West German regions (e.g., Kronthaler 2005; Uhlig 2008).
Germany did not take part in the GEM 2007 cycle.
This is indicated by the lower settlement structure-type number where (1) is a highly agglomerated area. For example, 18 % of all districts in West Germany are rural districts (type number = 4) in rural regions, while this is 38 % in East Germany.
Because of the nested structure of our data (individuals in regions), multi-level models would be an alternative regression technique. However, likelihood ratio tests comparing multilevel models to standard logistic regressions reject the hypothesis of random effects, recommending the use of standard logistic regressions (Hox 2010). Moreover, in the case of our paper results from multi-level models do not differ substantially from those of logistic regression. We, thus, use standard logistic regressions throughout the paper. For an assessment of regional start-up activity by means of multi-level modeling, see Hundt (2012).
Based on the estimates, the overall effect would turn to negative at an age of 76 years which is out of our sample range.
The results hardly differ when interacting the dummy marking West Germans not knowing an entrepreneur and the one for East Germans not knowing an entrepreneur with the age variable.
Interpreting interaction terms in nonlinear models is not straightforward. Using the more robust Ai et al. (2004) method confirms our initial interpretation. Results can be obtained upon request.
Introducing an interaction term for the proxy for knowing an entrepreneur and the non-treated group of East Germans (those born in 1975 or later) does not change the significant effect for the treatment group. The interaction for younger East Germans itself is insignificant. This means that the effect of knowing an entrepreneur is not much different for younger East Germans as compared to West Germans that know an entrepreneur whereas there is a significant difference for older ones.
The results are similar when including a dummy variable for younger East Germans born after 1975.
Having entrepreneurial intentions is negatively correlated with fear of failure (r = −0.1829) and significantly negative related to being a latent entrepreneur in the regressions. Fear of failure might of course determine the probability to have entrepreneurial intentions, but it might be also the other way round. So, there is reverse causality and we do not want to interpret the models that “control” for fear of failure any further. The point that we want to drive home is that including fear of failure in the specifications of Table 4 would not change the coefficient estimates for the main variables of interest.
The results are not published in the official GEM report for the 2014 wave but can be obtained upon request.
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We are indebted to Michael Fritsch, Thomas Grebel, and Elisabeth Bublitz for helpful comments.
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Wyrwich, M., Stuetzer, M. & Sternberg, R. Entrepreneurial role models, fear of failure, and institutional approval of entrepreneurship: a tale of two regions. Small Bus Econ 46, 467–492 (2016). https://doi.org/10.1007/s11187-015-9695-4
- Fear of failure
- Role models
- Peer effect
- Entrepreneurial intentions
- Global Entrepreneurship Monitor
- Communist legacy
- East Germany