Rural entrepreneurs and institutional assistance: an empirical study from mountainous Italy

Abstract

Despite the recognition of entrepreneurship as one of the main determinants of rural economic development, empirical research in this field is relatively sparse. Thus, there is little evidence on the role and function of rural entrepreneurs, the driving force behind the birth, survival and growth of rural enterprises. The present work aims at providing a contribution to filling this gap in knowledge. We present and analyse the results emerging from a questionnaire submitted to a sample of 123 rural entrepreneurs and businesses in a mountainous area of central Italy. In particular, we test for six hypotheses concerning the correlation between different factors, reflecting entrepreneur and business-specific characteristics, and the adoption of instruments of institutional assistance. Entrepreneur's and business's variables are related to (1) entrepreneurial human capital; (2) entrepreneur's local knowledge and social capital; (3) firm's size; (4) entrepreneur's age; (5) firm's age; and (6) busines's sector of activity. Empirical results largely support the importance of variables taken into consideration in explaining differences in the adoption of institutional assistance among businesses of the sample. In the light of our empirical findings, we also examine and propose potential policies for fostering entrepreneurship and the development of the rural region under study.

Keywords:

• rural entrepreneurship
• business survey
• human and social capital
• financial and non-financial assistance

Acknowledgements

The authors wish to thank Tom Barbiero, Carlo Bianchi, Giovanni Gallipoli, Theodore Panagiotidis, Dimitri Skuras, all the members of the EMASE (Entrepreneurship in Mountainous Areas of Southern Europe) project and participants in the 73rd EAAE seminar (Ancona 2001) and in the 2nd EEFS conference (Bologna 2003), where earlier drafts of this paper were presented, for helpful comments and suggestions. Special thanks are given to Bengt Johannisson, Editor of Entrepreneurship and Regional Development, and two anonymous referees who, through constructive criticism, have motivated us to crucial improvements and corrections. Last, but not least, we wish to thank Ciro Conversano for his invaluable help in collecting and organizing the data and Angela Pelloni for reading the last draft and providing invaluable remarks that spared us a few mistakes. Financial support from the European Union under the EMASE project, Contract no. FAIR 6-CT98-41, is gratefully acknowledged.

Notes

Notes

1. The importance of entrepreneur's human capital variables for small rural firms is studied in Bates (1990), Barkham (1994) and Skuras et al. (2005) in relation with the firm's longevity, size and growth, respectively. Instead, Davidsson and Honig (2003) analyse in detail the role of human and social capital among nascent entrepreneurs.

2. For instance, natural amenity areas attract vacationers and retirees.

3. These methods are often included under the label ‘financial bootstrapping’ (Winborg and Landstrom 2000), that is financial methods for meeting the need for resources without relying on long-term external financial assistance.

4. See Leland and Pyle (1977) for a theoretical introduction to the topic of asymmetric information in capital markets.

5. The lack of adequate entrepreneurial outlook and culture can also be illustrated by the cases of the textile and mining sectors. The shares of both sectors were large and, right until the 1980s, the textile industry was an oligopoly. This sector entered a severe crisis when entrance barriers were lowered and new competitors allowed to enter the market as a consequence of European Union agreements. One of the biggest companies in the area went bankrupt as it was unprepared to face genuine competition. In the mining sector the rapid changes brought about by technological advances and modern industrialization have radically modified the market structure. As in the case of the textile industry, weak and badly-organized companies, despite the high quality of their products, could not adjust to new competitive pressures and to changed market needs.

6. As an example, the Social Fund of the EU in co-operation with either national or regional authorities took care of training, while in many EU countries the Leader groups and local development authorities provided assistance for product development.

7. Non-response rates for other sectors have been: 46% for tourism and 50% for other services.

8. Regarding taxation issues, it is also important to note that at times Italian firms are reluctant to indicate the exact amount of non-regular workers’ employment. Agencies offering support are likely to ask for indicators of firm's size, cash-flow and employment, which are at times hidden by small Italian companies. This phenomenon might have sample effects.

9. It is worth noting that we have also estimated the model with dependent variables related to the specific utilized instrument of assistance (i.e. EU grant aids, national grant aids, subsidized interest rate commercial loans and marketing assistance, training assistance, etc.) but results were less significant or not significant at all (according to the assistance instrument considered). A possible explanation is that the businesses in our sample have used different (and often not simultaneously) instruments of assistance, thus results become relevant only if we at least aggregate with respect to type (financial or non-financial) of used assistance tool.

10. Obviously, average values (percentages) for dummy variables DFINSUPPORT and DNONFINSUPPORT are lower than that for DSUPPORT because some firms of the sample used only one type of assistance instrument (financial or non-financial).

11. We used the following weights to convert employment in full-time employees: full-time = 1; part-time < 50% = 0.25; part-time 50% = 0.50; part-time > 50% = 0.75; seasonal worker < 6 months = 0.50; seasonal worker > 6 months = 0.75.

12. The variables measuring entrepreneur's age and experience are usually expected to be highly correlated and thus are often not simultaneously included in regressions (Goodwin and Schroeder 1994). In our case, however, the two variables were not significantly correlated since the variable DEXPERIENCE refers to the previous experience of entrepreneurs before starting the actual business and this can be independent of the entrepreneur's current age.

13. We used the Stata statistical package for all our statistical analyses.

14. For independent dummy variables (e.g. DEDUC or DEXPERIENCE) the marginal effect is computed by comparing the probabilities that result when the variable takes its two different values with those that occur with the other variables held at their sample means (Greene 2000).