Abstract
This paper estimates the effect of education on the success of entrepreneurial activity, using survey data from Malawi. An instrument variable approach is used to address the endogeneity of education. We find a significant and substantial effect of an added year of primary education on entrepreneurial profitability. This is consistent with theoretical arguments that primary schooling provides a generalised form of competence that underpins the variety of skills an entrepreneur needs to succeed in business. Results are robust to non-random selection into entrepreneurship.
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Notes
Consistent with these arguments, Barouni and Broecke (2014) estimate wage returns of education for 12 African countries, and find a substantially higher effect of secondary and tertiary education than for primary education.
Agriculture constitutes 36 % of GDP in Malawi, and almost 60 % of Malawi’s exports stem from tobacco (Republic of Malawi/World Bank 2006). The expansion of the private sector is an aim of domestic industrial policy (IMF 2007; Record 2007), and education has been suggested as one possible means to making the private sector more profitable and productive (Republic of Malawi/World Bank 2006).
See for instance Orr (2000) on the ownership of Malawian estates.
While land may in principle be used as collateral for loans, facilitating access to capital, additional regressions showed no significant relationship between access to land and business related loans.
While one may argue that the education decision is usually made before the entrepreneurship decision, reversing the order of the first two stages produces very similar estimates of returns to education.
See www.worldbank.org/html/prdph/lsms/country/malawi04/docs/IHS2%20Basic%20Information.pdf for further documentation.
While one may question the accuracy of reported profits, this appears to be the best available indicator of entrepreneurial success (cf. Mel et al 2009). We have checked the consistency of this variable with reported revenues less costs, and the correlation is high (0.81). Using the log of profits means we lose 95 non-positive observations.
Rather than age, some previous studies use age minus years of schooling −6 as a measure of labour market experience, doing so does not change results. We have chosen not to include industry dummies in our estimations, as these are likely to be endogenously determined and influenced by education (cf. e.g. Wang 2013). A number of other possible control variables suggested by previous studies proved highly insignificant in preliminary estimations and have not been included in our main estimations. These include the number of household members working in business (another measure of firm size), ethnic minority status of the owner and the marital status of the owner.
In the full IHS2 data set, almost 50 % of urban inhabitants live in a household that has access to land (compared to more than 95 % in rural areas). The proportions are about the same in our sample. There is hence no reason to believe that the access to land instrument is not relevant in urban areas. Running the estimation of the selection equation for only urban dwellers, access to land and its square displays much the same relation to entrepreneurship status as in the full sample.
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Acknowledgments
The authors thank Erik Ø. Sørensen, Magnus Hatlebakk, Bertil Tungodden, Eyolf Jul-Larsen, Øivind Anti Nilsen, and two anonymous reviewers for valuable comments and advice. We are grateful to the National Statistical Office (NSO) of Malawi for providing the data. However, further processing and application of the data was the responsibility of the authors and the views expressed are those of the authors and not of the NSO.
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Kolstad, I., Wiig, A. Education and entrepreneurial success. Small Bus Econ 44, 783–796 (2015). https://doi.org/10.1007/s11187-014-9621-1
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DOI: https://doi.org/10.1007/s11187-014-9621-1