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The importance of family background and neighbourhood effects as determinants of entrepreneurship

Policies aimed at encouraging entrepreneurship are popular around the world, but a recent literature suggests that entrepreneurship might be more predetermined than previously thought. This column uses sibling correlations to tease apart the importance of genes, family background, and neighbourhood effects for later entrepreneurship. Parental entrepreneurship and genes are the two main drivers of sibling similarities in entrepreneurship. However, children do appear to be able to learn about entrepreneurship through their family and community, so it may be possible to teach relevant skills to young people.

Entrepreneurship has been hailed as an avenue for upward social mobility and a driver of innovation, job creation, and growth. As a result, policies aimed at encouraging successful entrepreneurship have been adopted in many countries. For example, vast amounts of money are spent in attempts to facilitate access to finance for entrepreneurs with great ideas but limited personal capital. Entrepreneurial education programmes are now popular – they have permeated academic and training curricula, from primary school, through tertiary education, and to active labour market programmes. Their goals may vary, but often come down to increasing participants’ levels of entrepreneurship-relevant skills – such as proactivity, business planning, or creativity – or alternatively, making them familiar with the possibility of careers in entrepreneurship.

One might suppose that entrepreneurship choices and outcomes are entirely predetermined when people are young. Alternatively, it could be the case that entrepreneurship is an entirely genetic matter. Or, in a third case, the relevant imprints could be made when people are young, either within their domestic environment or in their neighbourhoods. In each of these cases, entrepreneurship is determined by factors outside (adult) individuals’ control. In these cases, policies aimed at adults would simply miss their target, and would ideally be replaced by policies aimed at children or their parents. Of course, reality is not black and white, and the level of predetermination of entrepreneurship outcomes would be neither 100% nor 0%. Nevertheless, this thought experiment indicates that the potential success of policy measures and education programmes in entrepreneurship hinges, in part, on the assumption that individuals’ entrepreneurship selection and performance are not predetermined.

Interestingly, recent papers have collectively suggested that entrepreneurship might be more predetermined than previously thought – entrepreneurship education has been proven to be effective in primary school (Huber et al 2014) and, to a lesser extent, in secondary school (Elert et al 2015), but not at all when individuals are older, that is, students (Oosterbeek et al 2010) or adults (Fairlie et al 2015). Moreover, strong intergenerational associations in entrepreneurship have attracted considerable attention. While part of this relationship has been shown to be genetic (Nicolaou et al 2008), parental role-modelling appears to be the main driver of the intergenerational association in entrepreneurship (Lindquist et al. 2015). Additionally, exposure to a dense entrepreneurial environment during formative years also increases the likelihood of entry into entrepreneurship (Guiso et al. 2015).

So the policy-relevant questions arise: To what extent is entrepreneurship predetermined? Have we spent (public) funds wisely by implementing policy measures and education aimed at changing the behaviour of adult people?

In a recent paper, we assess the predetermination of entrepreneurship outcomes by calculating and analysing sibling correlations (Lindquist et al 2016). We argue that sibling correlations are more complete and precise measures of predetermination, including the importance of genes, family background, and neighbourhood effects as determinants of entrepreneurship. Sibling correlations have been used before to study outcomes other than entrepreneurship and provide much broader measures of the importance of family background and neighbourhood effects than intergenerational associations (Solon 1999). Their interpretation is also straightforward – the higher the sibling correlation, the larger the importance of family background.

Through the novel application of this measure of predetermination to various measures of entrepreneurship and entrepreneurial success, and applying an accounting exercise to study potential underlying factors, we can address questions such as: Do individuals start life with equal chances of engaging and succeeding in entrepreneurship? If not, then what family- and/or community-wide factors do children and young adults face that may later limit – or promote – their opportunities of becoming successful entrepreneurs?

To compute sibling correlations in entrepreneurship, we use detailed and rich data drawn from Sweden’s Multigenerational Register. We have extensive data on individual and family-wide socioeconomic variables pertaining to more than 700,000 siblings, including information on parental education and income, parental entrepreneurship, family structure, and parish of residence when individuals were young. To obtain a more complete picture of entrepreneurial outcomes, we model self-employment and incorporation separately, as they may capture different aspects of entrepreneurial engagement. Moreover, we analyse not only to what extent youth environment affects the decision to become an entrepreneur, but also to what extent it affects the ability to survive and thrive as an entrepreneur. Our main results show that:

  • 25% of the variance in individuals’ decisions to become self-employed is explained by family background and community influences;
  • For incorporation, this is close to 35%;
  • These percentages are slightly higher when we consider measures of successful entrepreneurship such as above median years of self-employment and incorporation;
  • Brother correlations are always larger than sister correlations;
  • The largest correlation is for males with above median years of incorporation, which is close to 50%;
  • Mixed sibling correlations are consistently smaller than same-sex correlations.

What makes siblings similar?

To get a deeper insight into what drives sibling similarities, we examine the roles played by other factors suggested by the literature, and the results are summarised in Table 1.

Table 1 Summary of results

To tease out the influence of regional variation in the availability of entrepreneurial role-models, we estimate neighbourhood correlations, and find that less than 10% of sibling correlations can be explained by neighbourhood effects. This suggests an impact of regional variation, but of limited scope.

To assess the role of parental characteristics in generating sibling similarities, we run an accounting exercise in which we re-estimate our sibling correlations controlling for observable parental characteristics. The results of this exercise show that:

  • Parental entrepreneurship status is quite important;
  • Parental education and income matter much less; and,
  • Family structure and immigrant status do not matter.

One of our most interesting findings is that:

  • Parental self-employment is a prime explanatory force in individual self-employment, but not incorporation; and
  • Parental incorporation explains individual incorporation best, but not self-employment.

This result constitutes further proof that self-employment and incorporation are different aspects of entrepreneurship (Henrekson and Sanandaji 2014, Levine and Rubinstein 2016), and that there are different transmission mechanisms depending on the type of entrepreneurial engagement of the parents.

To capture potential interactions between the siblings themselves, we first examine how correlations vary with the age difference between siblings. Unlike the bulk of the sibling correlation literature, where closely spaced siblings tend to have more similar outcomes than widely spaced siblings (e.g. Eriksson et al 2016), we find that sibling correlations are unaffected by birth spacing. This gives initial evidence that sibling peer effects are not very strong. In a formal model, we compute an upper bound on these effects by performing a correlated random effects exercise (Altonji et al 2016). Our estimated peer effects are generally non-significant and very small in magnitude – less than 10% of the sibling correlation could potentially be explained by sibling peer effects.

Lastly, to investigate the role of genes (in general) and shared genes (in particular), we use the information on twins in our dataset to perform a genetic decomposition exercise. Our results imply a relatively important role for genes, as shared genes may account for a large part of sibling similarity:

  • Between 56–78% of the sibling correlations in self-employment; and
  • Between 38–46% of the sibling correlations in incorporation.

Concluding thoughts

Our methods generate a novel measure of the extent to which entrepreneurial entry and success are predetermined by genetic and domestic factors when people are young. Our approach allows us to investigate a comprehensive set of potential mechanisms in order to ascertain the channels through which family environment influences children’s entrepreneurial choices and success. In doing so, we put many previous results in the literature – regarding the role of neighbourhoods, parental income, parental entrepreneurship, and genes – into perspective. Unlike previous studies, we are able to investigate the relative importance of various mechanisms within a unified empirical framework. We conclude that parental entrepreneurship and genes are the two most important factors generating sibling similarities in entrepreneurship.

Policy implications

We tend to view our findings optimistically. We do not believe that the existence of substantial, pre-determined family-wide factors means that policy is doomed to fail. A large share of the variation in entrepreneurship is, in fact, individual-specific, and not solely determined by genes. Furthermore, children appear to be able to learn about entrepreneurship through their family and community environment, which implies that it may be possible to teach entrepreneurship to young people. Policy may even generate a social multiplier effect if the behaviour of a successfully treated person also affects the behaviour of her untreated family members.


Altonji, J G, S Cattan and I Ware (2016) “Identifying sibling influence on teenage substance use”, Journal of Human Resources, forthcoming.

Elert, N, F W Andersson and K Wennberg (2015) “The impact of entrepreneurial education in high school on long-term entrepreneurial performance”, Journal of Economic Behavior and Organization, 111(1): 209-223.

Eriksson, K H, R Hjalmarsson, M J Lindquist and A Sandberg (2016) “The importance of family background and neighborhood effects as determinants of crime”, Journal of Population Economics, 29(2): 219-262.

Fairlie, R W, D Karlan and J Zinman (2015) “Behind the GATE experiment: Evidence on effects of and rationales for subsidized entrepreneurship training”, American Economic Journal: Economic Policy, 7(2): 125-161.

Guiso, L, L Pistaferri and F Schivardi (2015) “Learning entrepreneurship from other entrepreneurs?”, CEPR, Discussion Paper 10997.

Henrekson, M and T Sanandaji (2014) “Small business activity does not measure entrepreneurship”, Proceedings of the National Academy of Sciences, 111(5): 1760-1765.

Huber, L R, R Sloof and M van Praag (2014) “The effect of early entrepreneurship education: Evidence from a field experiment”, European Economic Review, 72: 76-97.

Levine, R and Y Rubinstein (2016) “Smart and illicit: Who becomes an entrepreneur and does it pay?”, Quarterly Journal of Economics, forthcoming.

Lindquist, M J, J Sol and M van Praag (2015) “Why do entrepreneurial parents have entrepreneurial children?”, Journal of Labor Economics, 33(2): 269-296.

Lindquist, M J, J Sol, M van Praag and T Vladasel (2016) “On the Origins of Entrepreneurship: Evidence from Sibling Correlations”, CEPR, Discussion paper 11562.

Nicolaou, N and S Shane, L Cherkas, J Hunkin and T D Spector (2008) “Is the tendency to engage in entrepreneurship genetic?”, Management Science, 54(1): 167-179.

Oosterbeek, H, C M van Praag and A Ijsselstein (2010) ”The impact of entrepreneurship education on entrepreneurship skills and motivation”, European Economic Review, 54(3): 442-454.

Solon, G (1999) “Intergenerational mobility in the labor market”, in O Ashenfelter and D Card (eds), Handbook of Labor Economics, 3: 1761-1800, Elsevier NV, Amsterdam.

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