Entrepreneur’s rate of return to education
Name student: Lorenzo Amin Student number: 10187332
Programme: Bachelor Thesis Economics Specialization: Economics and Finance Number of credits thesis: 12 EC
Abstract: This paper investigates the effect of higher education on entrepreneurial performance. Despite the fact that a lot of research is done on this subject, the reason to investigate this subject is twofold. First it is important for the potential
entrepreneurs to know whether education has an effect on their performance anyway. Beside that the data of this research is collected from a German Household survey to investigate the situation in Germany while most studies on this area are based on the United States data. First some general benefits of education are mentioned and after a regression analysis, it turns out that both higher education level and the years of education have a positive effect on entrepreneur’s performance. Further the
researcher has found evidence that male entrepreneurs are more successful than the female ones. In the literature, most researchers agree on the positive effect of education on entrepreneurial performance. Nevertheless there are some
disagreements with whether entrepreneurship selection is affected by education or not.
1. Introduction
Most people believe that entrepreneurial attitude is given and not created. At least once in your live you might have heard that someone wants to become an
entrepreneur but is advised to finish college first because college degree is more important. This is the common view that most people have, thinking that one first has to get graduated and then start a business. It this the right view?
While some entrepreneurs are graduated from a higher education, some did not even attend secondary school ever. Whether the ones with a college diploma perform better than the ones without is the central topic in this paper. What are the benefits of education? People choose to do an education to develop their skills further, to get recognition and most importantly to increase their future income. But do these qualities also have a positive effect on entrepreneurial performance? More explicitly, the central question of this research is whether higher graduated
entrepreneurs perform better than other lower-graduated entrepreneurs?
The reason for doing this research is twofold. First, if higher education does not have a positive effect on entrepreneur’s performance then why should some potential entrepreneur do a higher education anyway? So it is important to investigate this field for the potential entrepreneurs. Rejecting the hypothesis that higher
entire business school programs and other studies for entrepreneurs. Second, past studies focused on the US markets, this paper investigates the current literature on entrepreneurship further and investigates the current situation in Germany through a dataset collected from a German household survey of the year 2011.
First part of this paper will mention an overview of the general benefits of education. It is crucial to know that education has also other benefits like for example recognition besides potential income increases. The second part gives an overview of the history of this subject through a literature analysis. More specifically, in the second part the methods and models of measuring entrepreneurial performance are investigated. Also some common findings in the literature are mentioned in part three. In the fourth part dataset of a German household survey from 2011 is collected.
The survey consists data on income and other variables of 1104 entrepreneurs that will be used to investigate the effect of the educational level of entrepreneurs on their performance through a regression analysis. After that a descriptive analysis of the findings will be put forward to mention the results and then a conclusion will follow in part seven.
2. Benefits of education
Although the share of higher educated workforce has been growing in the Netherlands since the last years the earning potentials of higher education has decreased because of the financial and economic downturn (Exhibit 1). But why do people study and the share of higher educated workforce is growing despite lower earning potentials?
A major benefit of education is the lower risk of unemployment for higher educated workers. Education will cause potential income increases through
specialization in specific jobs. Education has benefits other than the income increases and lower risk of unemployment. People get more recognition through education and distinguish themselves in their performance from the lower ability workers.
Viewed by many scientists as the father of modern labor economics, mincer states that the only empirical measure of investment in human capital is limited to investment in schooling, measured by the years of schooling (Mincer, 1974). Most researchers use main ideas of his model in their research models, this makes him such important in this field. ‘The “basic” earnings function method is due to Mincer
(1974) and involves the fitting of a semi-log ordinary least squares regression using the natural logarithm of earnings as the dependent variable, and years of schooling and potential years of labor market experience and its square as independent variables’ (Psacharopoulos, 1994). In Mincer’s equation the years of schooling are taken as the investment in schooling without taking into account the level of education. Accordingly the cost of education is the foregone earning opportunities when attending school.
In Mincer’s equation, the logarithm of earnings is modeled as the sum of years of education and the quadratic years of experience (Mincer, 1974). Model specifications are:
ln (Y) = ln (Y0)+ rS + B1X + B2X2 where
Y stays for earnings
Y0 stays for the earning without schooling and experience
S indicates the years of schooling and
X indicates years of labor market experience (Mincer, 1974). As is mentioned above, most researchers use the mincer equation to investigate the return to schooling. Hamilton (2000) for example used the idea of Mincer Equation by regressing the years of education and experience on the log earnings. His model makes him famous in the field of modern labor economics.
According to Psacharopoulos (1994) primary education continues to contribute the highest social profitability in all world regions. The rate of return to education diminishes by the level of county’s per capita income (Psacharopoulos, 1994). However it is shown that the return to higher education has increased by 2-8 percent over 15 years. Further he shows that the return to education is higher for female participants compared to the male participants.
Looking per education category, according to Psacharopoulos (1994) the private rate of return to education is highest for engineering, law and economics while the lowest for physics, sciences and agronomy.
3. Past research and methodologies on entrepreneurial performance
Research on entrepreneurship found their origin in the 50s by Michigan (1945-1958) being the first type of its kind. In the period after WWII the study approached more researchers and among them are research papers that made it to the greatest economic
journals. This section overviews the primary method and findings of these researchers.
3.1 Definition of entrepreneurship
To distinguish entrepreneurs from non-entrepreneurs there is a need for the definition of entrepreneurship. For example, is Frank Morsh who has the hobby of painting and at the same time is a bartender partly entrepreneur?
The definition of entrepreneurship in the Netherlands is defined by the Dutch Tax agency as someone that produces products or services, asks a compensation for it, takes part of the economic activity regularly and is responsible for its own activity. This is a very broad definition and disassembling this definition will almost take half a book. May the literature give a good definition?
In the literature there have been difference between entrepreneurs and self-employers. Accordingly, entrepreneurs are defined as those who create companies, contribute new products and production processes (Robonson and Sexton 1994). Self-employment is a term that is used to various activities in the economy. Examples are those who run a business all alone without any employee (Martinez, Mora & Vila, 2007). The literature distinguishes entrepreneurship from paid-employed by being responsible for your own business (Hamilton, 2000).
In this paper by entrepreneur is meant, as someone who is self-employed and the definition is someone who works for his own business independent of how many employees are working for him or her.
3.2 How entrepreneur’s performance is measured
Performance is viewed as a relative thing. Is the level of income a determinant of performance or is it just the viability of a company the measure of performance for an entrepreneur? There are common regularities with respect to the measure of
performance.
Performance measured by the level of income for an entrepreneur relative to an employee is the common method used in the literature (Van Der Sluis, Van Praag & Vijvenberg, 2004). De wit and Van Winden(1989) investigated the determinants of
entrepreneurial performance and selection by using the natural logarithm of income as one of the primary variables.
Unfortunately none of these studies take the non-pecuniary benefits of the entrepreneurs into account. These benefits are substantial and will contribute to the entrepreneur’s welfare (Hamilton, 2000). Examples of these non-pecuniary benefits are being your own boss, easier to reschedule working time and more freedom at the work floor. Moreover, Hamilton (2000) used the expected present value of lifetime earning as a determinant of performance between self-employed and paid employed workers instead of current income.
This paper uses the previous year’s income of the entrepreneurs that
participated in the survey of 2011 as an indicator of performance. The reason for this is that data on income is easiest to get. However the disadvantage of this variable is that it may reflect a snapshot of the entrepreneur’s performance. Using live time income will reflect the whole picture of entrepreneur.
3.3 Education to entrepreneurs
Entrepreneurs perform many tasks. As follows from the example of Lazear (2004), consider the creation of a clothing label line. Besides sewing clothes, the founder must hire mode designers, follow the trend, possess knowledge of marketing, hire models and many other things. Being a good tailor is not enough. As the great
scientist Edward Lazear calls, the entrepreneur must be the Jack-of-all-trades (Lazear, 2004).
Lazear (2004) illustrates entrepreneurs as generalist and the employees as specialist. This follows from the fact that an entrepreneur has to do many tasks while the tasks of employees are restricted. Maybe this is why one often hears that business studies are much more universal than the other non-business studies. The purpose is to make potential entrepreneurs generalists. As a result entrepreneurs must be multi tasked (Lazaer, 2004).
As Lazear (2004) mentions, even if entrepreneurs are not endowed with the necessary skills to start a business, they can acquire or learn these tasks through education. Lazear (2004) has found that on average entrepreneurs study a more varied curriculum when they are in the program than those who end up working as an
There are some educational benefits for entrepreneurs. As (Van Der Sluis, Van Praag & Vijvenberg, 2004) mention, education may increase the reputation and recognition of people and this way the educated entrepreneurs may get access to capital easier through angel investors for example.
Although there is no literature evidence but another advantage may be that having a higher education degree may help you financially in bad economic times if the entrepreneur’s business goes bankrupt. While some entrepreneurs had the necessary capital to invest and begin a business, most of them did not. To get access to the necessary capital to invest, people choose to invest in education and work for few years to get experience and build up capital to invest. This may be an important reason for the potential entrepreneurs to invest in schooling.
Summarizing, education let entrepreneurs acquire many skills that is needed to be an entrepreneur. Entrepreneurs have a safety net when they do not perform well in their business. They can always choose to become an employee. For educated entrepreneurs it is easier to acquire the required investments from angel investors or by working after graduation they can build capital to invest.
4 Economic researches on the benefit of education for entrepreneurs
Although different but the main idea of most methods used in determining correlation is the use of econometric or statistic models. Literatures on entrepreneurial education use the main ideas of the Mincer equation to determine the effect of education on income.
De wit and Van Winden (1989) investigated the determinants for the choice between self-employment and paid employment in the Netherlands. In their research they had a unique data sets of individuals consisting childhood ability (IQ), family background variables, education and midcareer labor market variables. They used the so-called endogenous switching model to find the determinants of entrepreneurship selection. Taking income into account, they build a model that predicts
entrepreneurship if the outcome of the model is positive and paid employment otherwise.
With their so-called endogenous switching model they found that the probability of self-employment is positively related to the earning differentials of self-employed and paid-employed, although the significance level is rather low. A
relatively high score on the IQ test will increase the probability of becoming an entrepreneur. Further, employment status of the father strongly influences the decision to become self-employed. Becoming an entrepreneur is more likely if the father was also an entrepreneur.
In search to find if entrepreneurship pays Hamilton (2000) investigated the potential explanations in differences between incomes of entrepreneurs and paid employed ones. Using the 1984 panel data of 8771 male participants that participated in a survey to investigate the potential explanations in earning differentials of
entrepreneurs and employees. He uses the expected present value of the income as a determinant of selection in self-employment and paid employment. Other variables included in its model are experience, investment, agency, matching, learning, and compensating differential models that offer different predictions for the self-employment earnings differential.
He found a median earning differential of 35% lower for entrepreneurs over 10 years.Most entrepreneurs enter and stay in the business despite the lower earnings and growth potentials. Hamilton’s study suggest that non-pecuniary benefits of entrepreneurs are substantial such as ‘being your own boss’. Solely measured in present value, the median earning differential is 25% lower for entrepreneurs over 25 years. However these findings are biased because of the fact that only male
participants are surveyed. It is important to include female participants to the survey since their share in labor force has grown since 1986.
In their study on entrepreneurship Van de Sluis et al. (2008) investigated the effect of college degree on entrepreneurship selection and performance. Their approaches consisted of reviewing the main literature on this topic and further investigate it using a meta-regression analysis. In their Meta Regression Analysis they assessed which study characteristics contribute more to entrepreneurial performance. More specifically the idea is to access the main explanations for entrepreneurial selection, staying as an entrepreneur and performance.
First they found that the impact of education on entrepreneurial selection is insignificant. This is not in line with the hypothesis of De Wit and Van Winden (1989) that state that childhood ability and college degree have a positive effect on entrepreneurial selection.
Further they found that the effect of education on entrepreneurial performance is significant and positive. A 6.1% increase in the earnings for each additional year of
education was found. However, they did not take into account the level of college degree. One can do 8 years to get its middle vocational education degree.
They found that the effect of education on earnings is smaller for
entrepreneurs than for the employees in Europe, but larger in the USA. Related to the last founding, they found that the return to schooling is higher in the USA than for Europe and lower for non-white males. In line with the conclusions drawn from Hamilton (2000) the last hypothesis suggests that the long-term earning for employees with higher education is higher than that of the entrepreneurs.
Are entrepreneurs in general higher educated? Do people with higher education become more likely an entrepreneur? Does higher education help entrepreneurs succeed? Robinson and Sexton (1994) tested these hypotheses using data on education and experience of people. Using regression analysis on the variables Income, education and experience they could answer these questions.
Robinson and sexton (1994) find that the self-employed ones have more years of education than the employed ones. Employees had on average nearly 1 year less education compared to the self-employed ones.
They come with the conclusion that the probability of becoming self-employed increases significantly by 8% for each year of education which is in contrast to the conclusion of Van de Sluis et al. (2008) that state that the effect of education on entrepreneurial selection is insignificant.
The rate of return for self-employed ones turns out to be substantially higher for the self-employed compared to the paid-employed ones. This result is inconsistent with the earlier findings of Van der Sluis et al. (2008) and Hamilton (2000) that concluded that the rate of return of each additional year of education is higher for the paid-employed ones.
Further Robinson and Sexton (1995) found that the rate of return to experience for self-employed is significant and positive but weaker than a year of education. So a year of education has higher positive effect on the success of an entrepreneur compared to experience.
Martinez et al. (2007) used a large sample of young European workers 4 years after graduation to examine some elements that lead to the occupational decision to become an entrepreneur. Their focus was on educational and economical
One of their main findings is that entrepreneurship is more related to males while women workers are more participating in the labor market as an employee. This may be a reason why Hamilton (2000) used a survey only participated by male participants.
They also find that the entrepreneurship is most active among Italian higher graduated workers. Further they find that entrepreneurs and self-employers have relatively longer education duration and lower grades consistent with the results of Robinson and Sexton (1995). Hamilton (2000) does not support the result that self-employers have in general lower grades. She states that the lower performance of entrepreneurs is not explained by assuming that the lower ability workers enter entrepreneurship.
5. Data collection
One of the most essential concerns to do a good research is finding the appropriate data. This paper uses a dataset of the German Socio-Economic Panel (G-SOEP), from 2011, which is a longitudinal survey of approximately 21069 private households in the Federal Republic of Germany from 1984 to 2012. Variables include household composition, employment, occupations, earnings, health and satisfaction indicators. Of these households around 1104 operate as an entrepreneur and information about their gender, educational level, income, labor market experience and years of schooling is collected.
5.1 Gender of entrepreneur
The gender of the entrepreneur is taken into account as a dummy variable to measure the differences between male and female entrepreneurs. As the share of women in the labor market has increased since the last two decades. Taking women entrepreneurs in the sample makes the sample more representative for the population. Martinez et al. (2007) states that entrepreneurship is more related to males. With the gender information this hypothesis will be tested for Germany.
Most literature uses the income as an indicator for performance. That is indeed why the data on income will be used as an indicator for the performance of the
entrepreneur. In this paper the gross income of the participant a year before 2011 is an indicator of the entrepreneurs performance. The natural logarithm of the income is generated for the convenience of interpretation.
One might think that the duration of the company is a good representation of the company’s and therefore entrepreneur’s success. However this variable is not added in the survey because the duration gives less measurement information compared to the gross income.
5.3 Years and the level of education
The participant’s years of education and the ISCED-level of education is used to measure the effect of education on performance. ISCED will divide the educational levels in inadequately, general elementary, middle vocational, vocational plus ability, higher vocational and higher education. To measure the effect of higher education on performance, the education levels are divided in two groups by which a dummy variable is created. 1 stands for higher vocational and higher education and 0 otherwise. This will be the variable that can explain the difference in entrepreneur’s performance. More specifically, this variable is the main concern of this research. Does education have a positive effect on entrepreneur’s performance is tested with this variable.
Because of the close co-movements between the higher education and the years of education, the latter is included as a substitute to education level. Years of education are taken into account to measure the effect of a year of education on income.
5.4 Labor market Experience
The variable labor market experience is taken into the regression analysis as an independent variable. Robinson and Sexton concluded from their analysis that the rate of return to entrepreneurship is higher for a year of education compared to a year of experience. With the variable years of experience this paper tries to test the
6. Regression
To get an idea if higher education has a positive effect on performance of an
entrepreneur log income is regressed on the variables gender, education level, years of education and labor market experience. This is performed using the statistical software Stata. The dependent variable LnIncome is the natural logarithm of gross income of a participant either with higher education or not.
Because of the multicolinearity between the years of education and education level, one of these variables is dropped out in regression 2 and 3. In the second regression, years of education, gender and labor market experience are added as independent variables. The third model regresses Lnincome on education level, gender and labor market experience while years of education is dropped out.
6.1 Hypotheses
Remember the research question of this paper is whether education has a positive effect on entrepreneurial performance. This paper uses regression analysis to answer this question. However to answer the question there is a need for hypothesis. The following hypothesis statistically figure the specification of the test that is performed for each model:
Hypothesis 1. H0: BGen = BLE = BYE =BEXP = 0 vs. H1: BGen or BLE or BYE or BEXP > 0
In words, the hypothesis says that under null-hypothesis the effect of each independent variable on income is significantly not different from zero. The
alternative hypothesis says that the effect of either one of the variables on income is significant and higher than zero. Using t-statistics with a 5% significance level the answer will follow.
Hypothesis 2. H0: BGen = BYE = BEXP = 0 vs. H1: BGen or BYE or BEXP > 0
Hypothesis 3. H0: BGen = BLE = BEXP = 0 vs. H1: BGen or BLE or BEXP > 0
The steps and the logic of the second and third hypothesis is the same as mentioned above.
In this section a descriptive analysis of the data is put forward. From this analysis some conclusions will follow with regard to the effect of education on
entrepreneurship.
7.1 Summary statistics
This section gives an overview of the sample data. As is shown in exhibit 2. The mean gross income of the entrepreneurs is 3326,41 euro with a standard deviation of 3510,99 euro, which is quit high. Of the participants 60% are male entrepreneurs. The other 40% are female entrepreneurs.
Of the participants 54% is higher educated. The mean number years of schooling for entrepreneurs turns out to be 13,8 years with a standard deviation of 3.23 years. The average labor market experience of the entrepreneurs is 21,3 years with a standard deviation of 12,86 years.
7.2 outcomes
As is mentioned earlier, the first model regresses the effect of higher education, years of education, gender and labor market experience on lnincome. Exhibit 3 shows the results of this first regression analysis.
In this model the R-square is 0,1303 that means that 13,03% of the variation in lnincome is explained by the variance in the independent variables. The other variation of the difference in income may be explained by other factors.
With a signification level of 5%, income is 49% higher for male
entrepreneurs, income will increase by 3,84% for each year of education and 1.68% for each year of labor market experience. However, the effect of education level on lnincome is insignificant. This is due to multicolinearity as the correlation between level of education and years of schooling is 68,5%, what is quit high (exhibit 4).
To solve this problem the variable level of education is dropped out of the model and lnincome is regressed with one independent variables years of education, gender and labor market experience. With a significance level of 5% the alternative hypothesis of the second regression is supported.
In the second regression it follows that entrepreneur’s income increases by 4,83% for each year of education, income of male entrepreneurs is 49% higher and
each year of labor market experience will increase income by 1,71%. However the performance of the regression is slightly deteriorated, R2 decreases to 12,94%. The results are shown in exhibit 5.
In this third regression a dummy variable that indicates entrepreneur’s level of education is added to the regression analysis and the variable years of education is dropped out. The idea is to see if the higher educated entrepreneurs are more successful than the lower educated entrepreneur.
The overall performance of the model further deteriorates by adding the education level variable, R2 decreases from 12,94% to 12,37%. In this regression, a male entrepreneur significantly earns 49,3% more than the female entrepreneur. Income will increase by 1,55% if experience increases by 1 year. Further the effect of a year of education on income remains statistically significant and positive (t>1.96).
The effect of Higher education level on income turns statistically significant and thus different from zero (t>1.96). In other words, higher educated entrepreneurs earn 26,2% more than the lower educated ones. The results are shown in exhibit 6. This result may be explained by the fact that for entrepreneurs that are higher educated it is easier to get capital and have more investment opportunities, as is mentioned by Van der Sluis et al. (2008).
8. Conclusion
This paper investigates the effect of education on entrepreneur’s performance both by doing a literature analysis and a data analysis. The main question of this paper is whether higher education has a positive effect on entrepreneur’s performance. The data analysis is performed by collecting data from the German socio-economic panel in the year 2011.
Education in general has the benefits of lower risk of unemployment when higher educated (Mincer, 1974). Researchers investigated the effect of education on entrepreneurship selection, the effect of family background on becoming
entrepreneur, the differentials between entrepreneur’s earnings to that of employees, the effect of education on entrepreneur’s performance.
In short it turns out that the level of education has a positive effect on
entrepreneurs performance (van der sluis et al.). Further Hamilton (2000) states that the level of education does not influence entrepreneurial selection. Although Van der
Sluis et al. (2008) disagree with this result. Also family background has a positive effect on entrepreneurial selection (De wit en van Winden). Further Hamilton (2000) states that entrepreneurship is more related to males.
The results of the data analysis show that both the level of education (third regression) and the years of education (second regression) have a positive effect on the entrepreneur’s performance. Some of the main findings of this paper are reflected to the findings in the literature. Although the model is weak (low R-square), the results are statistically significant.
Van der Sluis et al. (2008) state that entrepreneur’s income increases by 6.1% for each year of education. This effect of a year of education on entrepreneur’s performance turns out to be significant and positive in the data analysis of this paper. The results of this paper state that an increase of one year of education will increase income by 4,8%. Van der Sluis et al. (2008) did not take into account the level of college degree in their research. This paper investigates the effect of higher education level on income. The effect of higher education on income turns out to be 26,2% and significant.
Hamilton (2000) concluded that entrepreneurship is more related to man. Indeed his hypothesis is supported. Male entrepreneurs are performing significantly better. Also, the share of male entrepreneurs is 60%. So the hypothesis that
entrepreneurship is more related to males is supported since there are more male entrepreneurs.
As a conclusion the level of education has a positive effect on entrepreneur’s performance, at least in the region where the data is collected. This conclusion may be generalized to the state Europe since the regularities between Europe countries is substantially.
9. Further research
As of now, the effect of an economic education on entrepreneurship is not yet been investigated (Van Der Sluis, Van Praag & Vijvenberg, 2004). It would be worth to investigate whether these studies have contributed more to entrepreneurial
performance compared to other studies. However, this paper has been cautious with this question because the data on this subject is very hard to collect.
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Martinez, D., Mora, J. G. & Vila, L. E. (2007). Entrepreneurs, the self-employed and employees amongst young European higher education graduates. European Journal of Education. Vol. 42, pp.1-19
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EXHIBIT 1
Exhibit 2
Exhibit 4
