Introduction

Given the importance of entrepreneurial activity for economic growth (Wennekers and Thurik 1999; Acs and Storey 2004; Audretsch and Keilbach 2004; Van Stel et al. 2005; Wennekers et al. 2005; Fernández-Serrano and Romero 2014; Lecuna et al. 2017), interest has been steadily growing in analysing entrepreneurship. Economic and social development needs entrepreneurial agents, hence a growing interest by both public authorities and researchers; the former to encourage entrepreneurship and the latter to learn more about this phenomenon.

However, as important as it is to create new companies, it is even more important to ensure their continuation in order to guarantee the creation of work and wealth. So, knowing what the determinants of survival are for a new business is crucial.

Amit and Muller 1995consider that necessity entrepreneurs are those who lose their jobs and, faced with little prospect of finding a new job, decide to become self-employed. On the contrary, opportunity entrepreneurs are those who are motivated to start a business activity in order to exploit a potential opportunity. According to Amit and Muller, opportunity entrepreneurs are more likely to succeed than those of necessity and their empirical results corroborate this. In a similar vein, Caliendo and Kritikos (2010) also show that opportunity entrepreneurs are more likely to survive than those of necessity. Carrasco (1999) and Muñoz-Bullón and Cueto (2011) show, for Spain, that the chance of a new business surviving is reduced if the entrepreneur suffered periods of unemployment prior to starting the business, and more so if these were of extended duration. However, Block and Sandner (2009), after allowing for the education level of the entrepreneurs, do not find any differences in the survival of opportunity and necessity entrepreneurs.

The aim of this paper is to identify the determinants of survival in entrepreneurship in order to highlight which variables should be promoted by public authorities to ensure new businesses last longer. Also, in light of the literature relating to differences in the survival rate of necessity entrepreneurs and opportunity entrepreneurs, the factors that might explain this difference are also analysed.

The paper is divided into six sections. Following the introduction, the second section discusses the theoretical framework. The third section presents the methodology and then the fourth section analyses the data used. Section five shows the empirical results obtained and, finally, the last section contains the main conclusions of the research.

Theoretical framework

There are various socio-economic factors that the empirical literature considers as determinants for entrepreneurial survival. In this section, the factors considered in this paper are presented.

Recent literature has, on the whole, looked at the age and gender of entrepreneurs as socio-demographic factors that could affect business survival (Fairlie and Robb 2009; Millan et al. 2012; Kalnins and William 2014). With regards to age, this could be considered a proxy of the human and financial capital. Therefore, one might expect older individuals to have a higher accumulated human and financial capital and be more likely to survive in their business venture. In terms of gender, Millan et al. (2012) consider that women form a minority among the self-employed but that once women have overcome initial start-up problems, there is no reason why their entrepreneurial success rate should be any different to that of men. In this sense, Kalleberg and Leicht (1991), Brüderl and Preisendörfer (1998) and Oberschachtsiek (2008) show that gender has no significant effect on the survival of a business activity. However, Giannetti and Simonov (2004) and Cabrer-Borrás and Rico (2017) show that men are less likely to remain self-employed than women. In spite of the previous evidence, most literature shows higher survival rates for businesses created by men than by women. This is corroborated by Georgellis et al. (2007), Fertala (2008), Block and Sandner (2009) and Millan et al. (2012), among others, who demonstrate that men show a greater probability than women of business survival.

When looking at the impact of family life on entrepreneurs, as highlighted by Millan et al. (2012), the survival rate of the business activity is not clear. On the one hand, having young children would suggest a distraction of time and resources that could negatively affect business survival. On the other hand, it could act as a motivating factor to ensure business survival.

As far as nationality is concerned, many research papers show that foreign workers have a tendency to become entrepreneurs (Borjas 1986; Clark and Drinkwater 2000 and 2010; Lofstrom 2002; Schuetze and Antecol 2006; Fairlie and Lofstrom 2013). This greater propensity of foreigners to start a business could be because of the difficulty of breaking into the job market, or in co-validating qualifications or the desire to achieve success quickly in order to return to their native country as soon as possible. However, when comparing the survival rate of businesses between foreigners and nationals, the literature generally shows that the rate for immigrant workers is lower than for native workers (Lofstrom and Wang 2006; Fertala 2008; Andersson 2010).

Human capital is also considered a determinant factor of survival. Human capital can be thought of as the level of education of the entrepreneurs as well as the knowledge acquired through their previous work experience. According to Block and Sandner (2009), the effect of education on the probability of survival in self-employment is unclear. On the one hand, the theory of human capital would indicate that education has a positive effect on the probability of survival of the enterprise. However, on the other hand, entrepreneurs with high levels of education may have more opportunities for salaried employment than entrepreneurs with a lower education level, and this may reduce their time spent in self-employment. This argument is also true when considering the level of previous work experience of the entrepreneurs.

Looking at results obtained in previous research studies, Haapanen and Tervo (2009), Block and Sandner (2009), Andersson (2010) and Millan et al. (2012) reaffirm that education is a determinant in the duration of self-employment. Conversely, Georgellis et al. (2007) show that education is not a relevant factor. Finally, Nafzige and Terrell (1996), with research carried out in India, and Nziramasanga and Lee (2002), in Zimbabwe, find a negative relationship between education and the duration of self-employment.

With respect to empirical evidence relating to previous work experience, Taylor (1999), Georgellis et al. (2007) and Millan et al. (2012) show that previous experience positively influences the survival rate. However, Brüderl et al. (1992) and Van Praag (2003) see no relationship between previous experience and business survival. Haapanen and Tervo (2009), using data from the economy of Finland, show that previous experience has a significant negative effect on the duration of self-employment. Roberts et al. (2013) state that wide ranging experiences gathered from different organisations are associated with negative results in entrepreneurship. There is no reason to assume that working in a large number of companies would guarantee a higher level of knowledge and skills in the workers. Munsasinghe and Sigman (2004) show that the salaries of those workers who regularly change their workplace are systematically lower than more stable workers. This fact could be to do with a lower quality of human capital in workers who constantly change their job. A constant change is probably associated with difficulties in adapting to the new job, a prerequisite essential for acquiring skills and knowledge that could be useful when starting a new business.

Fritsch et al. (2006) analyse the effect that the productive sector and the geographical area have on business survival rates. For these authors, the survival rate is reduced in sectors where there is greater competition. However, Millan et al. (2012) note that the results seen in the relevant literature are very diverse and inconsistent, so it is difficult to establish a priori a relationship between the productive sector and survival. In addition, regional characteristics could also play a determining role in the survival rate of new businesses.

Amit and Muller 1995 and, since 2001, the Global Entrepreneurship Monitor have looked at the differences between necessity and opportunity entrepreneurs (Reynolds et al. 2002; Sternberg and Wennekers 2005), and found that survival depends on what initially motivates entrepreneurs to start their business. Much of the current literature looks at the motivation for becoming an entrepreneur and its impact on business survival (Block and Sandner 2009; Caliendo and Kritikos 2010; Muñoz-Bullón and Cueto 2011; Millán et al. 2014). Generally, companies created by entrepreneurs who could not find a salaried job have a lower survival rate than those companies created by entrepreneurs motivated by other reasons than simply employment. According to Cordura (2006), necessity entrepreneurs do not usually give the same consideration as opportunity entrepreneurs do to the factors for survival.

Methodology

In order to analyse the determinants that help an entrepreneur maintain his business activity, a binary choice model is specified and estimated, where the probability of survival depends on the personal, work and economic characteristics of the entrepreneurs (Mc Fadden 1973; Maddala 1983).

In the literature, there are different approaches to the economic interpretation of discrete choice models. Among the most outstanding approaches is the perspective of the utility theory in which the alternative selected by individuals is that which maximizes the expected utility.

In this approach, a rational individual will opt for the decision that allows him to choose between two exclusive alternatives, either (1) or (0), whichever will maximize the expected utility offered by each alternative. The i-th individual will choose option (1) if its utility Ui1 is greater than the utility provided by option (0), which is Ui0. Such a comparison, from the mathematical point of view, can be expressed through the following probabilistic inequality Prob(Ui1 > Ui0). In our case, if the utility of maintaining the business activity is higher than that of not maintaining it, the individual will opt for maintaining it.

The way to quantify the utility is to assign a probability to the rational decisions. This is done by means of the following equation:

$$ {P}_i= Prob\left({Y}_i=1\right)= Prob\left({U}_{i1}>{U}_{i0}\right)=F\left({X}_i\beta \right) $$

Where F(X i β) is the distribution function evaluated for the characteristics associated with the individual (i). The vector of observations of the characteristics is denoted by (X i ) while (β) is the vector of coefficients.

Thus, if a Logit model is assumed, the modelling of the choice of individuals can be done through the following behavioural equation:

$$ {P}_i=F\left({X}_i\beta \right)=\frac{e^{X_i\kern1em \beta }}{1+{e}^{X_i\kern1em \beta }}=\varphi \left({Z}_i\right) $$
(1)

The proposed model will serve to determine the relevance of the various factors that influence an individual when choosing whether to maintain their business activity.

In short, the specified Logit model is:

$$ {Y}_i=\varphi \left({Z}_i\right)+{\varepsilon}_i $$
(2)

where Y i is a dichotomous variable that takes the value one if the individual maintains his business activity and zero if he does not, while Z i is the index composed of the combination of coefficients and characteristics associated with individual i, such as personal, work and economic characteristics.

After the estimation of the Logit model, the decomposition proposed by Yun (2004) is applied. The original method proposed by Blinder (1973) and Oaxaca (1973) for decomposing uses two components. The first component computes the difference in the explanatory variables observed between the two groups and the second component shows the difference in the unobservable characteristics, quantified by the discrepancy in the parameters of both groups.

The objective of the decomposition method is to determine which part is due to the difference in the explanatory variables of both groups and which part to the difference in the repercussions that these characteristics have on the endogenous variable. The Blinder-Oaxaca method is intended for linear models but can also be applied to non-linear models. In particular, the method proposed by Yun (2004) allows decomposition to be performed for any type of functional relationship and to calculate the contribution of each variable. Although the Blinder-Oaxaca method presents identification problems in the presence of dummy variables, Yun (2005) proposes the correction of the identification problem by a normalized regression.

According to Yun (2004), if the probability of maintaining business activity is P i , for individual i, through a Logit model, the following can be stated:

$$ {P}_i=F\left({X}_i\beta \right) $$
(3)

The decomposition of the difference of the probability of survival among the groups formed by the opportunity entrepreneurs (O) and the necessity entrepreneurs (N) is:

$$ \overline{P_O}-\overline{P_N}=\left(\overline{F\left({X}_O{\widehat{\beta}}_N\right)}-\overline{F\left({X}_N{\widehat{\beta}}_N\right)}\right)+\left(\overline{F\left({X}_O{\widehat{\beta}}_O\right)}-\overline{F\left({X}_O{\widehat{\beta}}_N\right)}\right) $$
(4)

The first calculation reflects the difference explained by the distinct characteristics of each group, given the same coefficients. The second calculation, however, indicates the unexplained difference, that is, the part corresponding to the different response of the two groups with the same characteristics. Therefore, the Yun method will enable the decomposition of the difference in the probability of survival under a characteristic effect and under a coefficient effect.

In order to obtain the decomposition, Yun (2004) proposes a transformation in two stages. In the first, the Logit model is estimated for the mean values of the regressors and, in the second, a Taylor expansion of the first order is performed to linearize the effects associated with the characteristics and the coefficients around the mean, thereby obtaining the following expression:

$$ \overline{P_O}-\overline{P_N}={\sum}_{j=1}^k{W}_{\Delta X}^j\left(\overline{F\left({X}_O{\widehat{\beta}}_N\right)}-\overline{F\left({X}_N{\widehat{\beta}}_N\right)}\right)+{\sum}_{j=1}^k{W}_{\Delta \beta}^j\left(\overline{F\left({X}_O{\widehat{\beta}}_O\right)}-\overline{F\left({X}_O{\widehat{\beta}}_N\right)}\right) $$
(5)

where the weights of each variable j on the differences in characteristics and coefficients, respectively, are:

$$ {W}_{\Delta X}^j=\frac{\left({\overline{X}}_O^j-{\overline{X}}_N^j\right){\widehat{\beta}}_N^j}{\left({\overline{X}}_O-{\overline{X}}_N\right){\widehat{\beta}}_N} $$
(5a)
$$ {W}_{\Delta \beta}^j=\frac{\left({\widehat{\beta}}_O^j-{\widehat{\beta}}_N^j\right){\overline{X}}_O^j}{\left({\widehat{\beta}}_O-{\widehat{\beta}}_N\right){\overline{X}}_O} $$
(5b)

and the sum of all the weights is equal to the unit.Footnote 1

It should be noted that the decomposition is not invariant to the reference group being used. The literature proposes using the average coefficients of the two groups (Oaxaca and Ransom 1994; Fairlie 2005).

Data

The data used in this paper come from the Continuous Working Life Sample (CWLS). It is a database with individual information on more than one million workers and pensioners in Spain, drawn from the Spanish social security records, the Continuous Municipal Register and the Tax Agency. The CWLS is a representative sample of all individuals registered with the Spanish social security system in any given year.Footnote 2 In addition to work records, related to work carried out in Spain, the CWLS contains personal data on the individual, such as date of birth, address, gender, nationality and province of residence. A work record provides information on social security regime, start and finish dates of contracts, type of contract, working regime, contribution bases, reasons for contract termination, and production sector of their of activity.

The information used covers the period from 2011 to 2013. This information allows us to define both groups of entrepreneurs: those who succeed in maintaining their entrepreneurial activity and those who do not. The endogenous variable is defined as a dichotomous variable that takes the value one if the entrepreneur maintains the business activity at the end of the considered period and zero if the entrepreneur does not maintain the business.

The variables or characteristics used to explain the probability of survival of entrepreneurship are:

  1. 1.

    Personal characteristics: age, gender, nationality, educational level, number of children under twelve years old and the Autonomous Community (AACC) of residence.

  2. 2.

    Work characteristics: production sector of their activity and work experience.

  3. 3.

    Economic characteristic of the entrepreneur: social security contribution bases.

  4. 4.

    GDP per capita of the AACC of residence.

With regards to the measurement of variables, nationality is collected through a dummy variable that takes value one for a worker who is Spanish and zero for other nationalities. The gender of the workers is quantified through a dichotomous variable that takes value one for a man and zero for a woman. For the work experience, both the experience of the worker as self-employed and as employee is taken into account. Regarding the production sector, eleven production branches are considered: agriculture, industry, construction, commerce, transport, hostelry, finance, professional activities, education, health and other production areas. Finally, three levels of studies are considered: primary, secondary and higher.

Following the example of Block and Sandner (2009), self-employed workers are classified according to how they finished their previous salaried employment. Opportunity entrepreneurs would be those who voluntarily left their jobs to set up a business. The availability of information on the previous trajectory of the entrepreneurs helps us to find out how long ago they stopped their previous activity and became unemployed. It also helps us establish whether the termination of their position was voluntary or not and, therefore, which workers are opportunity entrepreneurs. Opportunity entrepreneurs are considered those workers who:

  1. 1.

    Make a direct transition from employment to self-employment (within a maximum period of 180 days), having voluntarily ceased their previous employment.

  2. 2.

    Start a business activity before leaving a salaried job (Block and Landgraf 2016).

In contrast, necessity entrepreneurs are those workers who initiate a business activity after losing their job or who become self-employed after being unemployed for more than six months.

Descriptive analysis of the data

Table 1 shows the descriptive statistics of the data used for the full sample as well as the entrepreneurial typology.

Table 1 Descriptive statistics of the data
Full size table

From a sample of 50.084 entrepreneurs, 78.89% are opportunity entrepreneurs, based on the definition proposed by Block and Sandner (2009). The percentage of entrepreneurs who were still in business in the early part of 2014 is around 70%.

With regard to the gender of entrepreneurs, the figures corroborate the general view expressed in the empirical literature that the percentage of male entrepreneurs is higher than that of women. When differentiating between necessity and opportunity entrepreneurs, the percentage of women among necessity entrepreneurs is higher than that among opportunity entrepreneurs (see Table 1).

Regarding the nationality of the entrepreneurs, the percentage of foreigners who start a business in Spain is 13.68%, rising to 17.34% in the case of necessity entrepreneurs. Around 50% of the entrepreneurs live in towns with more than forty thousand inhabitants.

As for education level, half of the entrepreneurs have secondary education and, moreover, the percentage of necessity entrepreneurs with higher education is slightly greater than that of opportunity entrepreneurs.

Finally, as far as social security contributions are concerned, these are much higher in opportunity entrepreneurs than in necessity entrepreneurs.

Results

Firstly, a Logit model is specified and estimated in which the probability of survival in entrepreneurship is determined by the following characteristics: gender, age, social security contributions, nationality, previous work experience, education, the production sector and the size of the town of residence. Also, the GDP per capita of the AACC of residence has been included as a proxy variable of the economic cycle.

The Logit model has been estimated both for the total of the sample and for necessity entrepreneurs and opportunity entrepreneurs, for comparative purposes (see Table 2).

Table 2 Estimation logit model
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The results of Model (1) in Table 2 indicate that there is a positive differential effect on the probability of business survival for opportunity entrepreneurs compared to necessity entrepreneurs. As for gender, being a male entrepreneur does not have a significant differential effect on business survival compared to being a female entrepreneur. Regarding nationality, Spaniards show greater business survival than foreigners in Spain.

Having children under the age of twelve positively affects the likelihood of continuing in the business activity.

As for the age variable, which is statistically significant, the positive sign indicates that as the age increases individuals are more likely to survive in their entrepreneurial business activity. However, the negative sign of the age squared coefficient indicates that the survival rate increases with age, but at a decreasing rate of change. The age at which the probability of business survival reaches a peak is at 45 years old.

Previous work experience negatively influences the probability of business survival. This result corroborates the evidence of Haapanen and Tervo (2009) and Roberts et al. (2013), reaffirming that previous work experience has a negative result on entrepreneurship.

A higher base of social security contribution suggests a greater probability of business survival, which could be explained by the higher opportunity cost of ending the business activity. With reference to the economic cycle, it positively affects the probability of survival of entrepreneurs.

In relation to education, having secondary studies positively affects business survival probability. This result is due to the fact that, to maintain a business, entrepreneurs need some basic academic skills, so having secondary studies positively affects the survival rates compared to having primary level education. By sectors, the results indicate that all the production sectors have a negative differential effect compared to the reference category, with the construction sector having the lowest probability of survival.

When estimating the model for the groups of necessity entrepreneurs and opportunity entrepreneurs separately (see Table 2 Models (2) and (3)), a substantial change in the coefficient of some factors is observed. The difference due to nationality is greater in necessity entrepreneurs than opportunity entrepreneurs.

The coefficients of the education variable are not statistically significant in the opportunity entrepreneurs, and so education does not influence in the probability of survival in this group. However, for necessity entrepreneurs, a secondary educational level implies greater probability of survival.

In both groups, age increases the probability of survival in business. However, the age at which the probability of business survival reaches a peak is 47 in opportunity entrepreneurs and 40 in necessity entrepreneurs.

The survival of necessity entrepreneurs does not seem to be influenced by the economic cycle situation, whereas it is a determinant and positive factor in the probability of survival of opportunity entrepreneurs.

Finally, the negative effect that previous work experience has on the probability of survival is greater in opportunity entrepreneurs than in those of necessity. This result implies that the labour market would reward the previous work experience of opportunity entrepreneurs more than necessity entrepreneurs.

Reference categories: (1) Female, (2) Foreign, (3) Primary, (4) No experience, (5) Other sectors. Significance level: *** p_value<0.01, ** p_value<0.05, * p_value<0.10.

Even when allowing for personal, labour and economic factors, there is still a visible gap in the survival rate of the entrepreneurial activity depending on the motivation behind starting the business. In order to identify the factors responsible for this difference, the Blinder-Oaxaca decomposition, developed by Yun (2004), is applied. The results obtained from the decomposition are shown in Table 3.

Table 3 Detailed Blinder-Oaxaca decomposition with distributed bias
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The difference in the probability of survival among the group of necessity entrepreneurs and those of opportunity is 0.91 percentage points, in favour of opportunity entrepreneurs. In distinguishing between the observable and unobservable component, it is apparent that if the necessity entrepreneurs had the characteristics of the opportunity entrepreneurs, the probability of survival of the necessity entrepreneurs would increase by 1.29 percentage points. The difference in probability of survival among necessity and opportunity entrepreneurs would be explained only by the observed component. The unexplained component is not significant.

The factors of social security contributions, nationality and a secondary education appear to have a greater positive effect on the survival of necessity entrepreneurs when they have the same characteristics as opportunity entrepreneurs reducing the survival gap between necessity and opportunity entrepreneurs. However, previous work experience and the production sector increase the survival gap between both groups.

Conclusions

The aim of this paper is to identify the determining factors that influence survival in entrepreneurship. It also aims to provide empirical evidence on whether the difference in the survival of necessity and opportunity entrepreneurs can be explained by the different characteristics of both groups, or if there are other unobservable factors that explain, under equal conditions, the greater entrepreneurial survival of opportunity entrepreneurs compared to necessity entrepreneurs.

The results obtained, through the binary choice model Logit, show that, with regards to entrepreneurial survival, there are no differences due to gender. However, Spanish entrepreneurs show a better business survival rate than foreign entrepreneurs in Spain.

The age variable positively influences business survival, reaching a turning point around 45 years. Also, having a secondary education increases the probability of business survival. However, segregating the sample between necessity and opportunity entrepreneurs shows education is a determinant of the survival of necessity entrepreneurs, whereas it is not in opportunity entrepreneurs.

Previous work experience negatively influences the probability of business survival. A varied work record could suggest that the potential entrepreneur finds it difficult to adapt to new work situations, yet this adaptability is an essential prerequisite for acquiring skills and knowledge that could to be useful when starting a new business.

With respect to the economic cycle, the probability of survival of necessity entrepreneurs is not determined by the situation of the economic cycle, whereas it does affect the survival of opportunity entrepreneurs.

The analysis of the decomposition of the difference in the probability of entrepreneurial survival between necessity and opportunity entrepreneurs leads us to conclude that observable factors imply a positive gap in business survival in favour of opportunity entrepreneurs, but unobservable factors are not significant.

After analysing the results, there are a number of implications for new businesses in terms of guidance to ensure their survival. One recommendation would be encourage entrepreneurship but not indiscriminately. It would be better to encourage entrepreneurial activities of opportunity rather than encouraging those who simply want a way out of unemployment. This is a complicated action but organizations that are involved in studying business plans could be better trained in advising potential entrepreneurs.

Another policy recommendation would be to promote entrepreneurial training. Curricular courses of entrepreneurship should be implemented in vocational training, in secondary education and in university education in order to ensure that more people start a new venture as an opportunity and not just as a way of having a job. In Spain, a number of regulations have been developed for the promotion of education in entrepreneurship, but there is still significant room for improvement in aspects such as the methodology used, teacher training and collaboration in the business world.

Additional help should be given to young people and older entrepreneurs. As has been shown, the relationship of age with the probability of survival is in the form of an inverted “u” with a peak at 45 years old, and so help should be directed towards those people at the extremes of this scale. Help could come in the form of easier access to finance or payments for the social security for any workers employed as well as subsidising social security payments for entrepreneurs themselves during their first year of activity.

In line with the European Commission’s Action Plans for 2011 and 2013 to improve access to finance for SME (small and medium-sized enterprises) and the Action Plan for Entrepreneurship 2020 respectively, a number of laws have been approved in Spain to support entrepreneurs and encourage business financing.Footnote 3 In spite of these advances, further progress is necessary in the field of fiscal policy to simplify tax burdens. With regard to financing for entrepreneurship, a review and simplification of administration processes is required, as well as a greater coordination of administrations, since many of the incentives offered to encourage entrepreneurship are territorial or local.