Factors Influencing the Choice of Entrepreneurship: Evidence from Dhaka City

Ashrafun Israt Ria
M.S.S. Economics, Department of Economics, Varendra University, Rajshahi, Bangladesh. E-mail: ishratria6@gmail.com

Nasrin Islam
Assistant Professor, Department of Economics, Varendra University, Rajshahi, Bangladesh; and is the corresponding author. E-mail: islamnasrin059@gmail.com

In Bangladesh, entrepreneurship development is especially significant for its intrinsic role of creating new employment opportunities, income generation and ultimately poverty reduction. This study aims to identify the factors that influence an individual to be an entrepreneur. The underlying factors are categorized as push associated with some negative forces and pull linked with some positive forces. Primary data was collected from 57 respondents from among the individuals engaged in various entrepreneurial activities. The ownership style, nature of enterprise, experience, educational qualification, and push and pull factors of the sample enterprises were considered as the main sources of primary data. The data analysis has been performed in two ways: Average response of the respondent on a statement and Principal Component Analysis (PCA) using STATA tool. Based on a comparison between the outcome of "Average response of the respondent on a statement" and "PCA of the responses", "Curse of unemployment" has been identified as the push factor that compelled the respondents to become an entrepreneur. On the other hand, "Strong willpower to do something on own","Self-employment and economic freedom","Use of personal knowledge and previous experience" and "Career advancement" have been found to be the leading pull factors that encourage respondents to become entrepreneurs.

Introduction
Entrepreneur by definition is an individual who starts a business by taking the liability of all the risks and uncertainties and keeping the expectation of earning profit in the future. The definitions of entrepreneurs and entrepreneurship are many and can be traced back to the last century. Cantillon (1755) defined entrepreneurs as those who gain non-fixed and uncertain amount of income under known costs of production (Tarascio, 1985). Later, Say (1803) described an entrepreneur as someone who transfers the commercial resources from areas that have low productivity and profit to higher ones. The official definition of entrepreneurship in the Oxford dictionary is the activity of establishing a business and wish for profit, while taking financial risks. Shane and Eckhardt (2003) defined entrepreneurship as "an activity that involves the discovery, evaluation and exploitation of opportunities", which is seen as one of the modern and acceptable definitions of entrepreneurship.

Entrepreneurship plays a vital role in the progress of any country. Schumpeter (1934) attributed the principal role in the balanced development to the entrepreneurs. Entrepreneurial actions are the main mechanism in the process of economic development.

The entrepreneurs are considered the main actors in the competitive environment, as competition between them leads to a reduction of costs through economic losses and reduction in the value of goods and services. Many modernization processes through the introduction of advanced technologies are a result of entrepreneurship development. For many years, the European Society considered entrepreneurship as a secondary activity, unworthy for people with high social status.

What motivates an individual to be an entrepreneur? Different studies have been undertaken to know the reason behind the motivation. Brophy (1989), Hisrich and O'Brien (1982), Brush (1990), Hughes (2006) and Praag and Cramer (2001) found that people opt for entrepreneurship if the expected rewards exceed the income from employment. Baumol (1990) advocated that entrepreneurs are encouraged by their allocation structure in the economy.

The factors which are contributing to the development of entrepreneurship can be broadly divided into 'pull' and 'push' elements (Turner, 1993; and Epstein, 1993). Regardless of the fact that ambitions differ among individuals on the basis of their personal characteristics, pull factors include all those positive and desirable reasons which attract or pull the entrepreneurs to their choice. Also, entrepreneurs are not always influenced by the pull factors; there are some factors which compel people to select entrepreneurship, which are known as push factors.

The latest Labor Force Survey (LFS) 2016-17 of the Bangladesh Bureau of Statistics (BBS) shows that about 44% of workers are entrepreneurs. They survive on their own earnings and do not receive any formal salary for the work performed. Labor force survey data for Bangladesh shows that between 2005 and 2017, 43.7% increment happened in the average weekly real wage for paid employees, whereas for entrepreneurs, it increased by 105%.

It has been clearly observed from different studies that the push or pull factors that affect the intention to be an entrepreneur vary from person to person as well as culture to culture. In this background, the study attempted to identify the push associated with some negative factors and pull inked with some positive factors that affect an individual to be an entrepreneur.

Objective
The objective of the study is to identify the factors influencing an individual to become an entrepreneur. The study attempts to investigate the factors (pull) that motivate the entrepreneurs to start and run their business and to examine factors (push) that lead the entrepreneurs to pursue entrepreneurship.

Data and Methodology
A total of 57 respondents were selected to assist the survey work. The respondents were selected from among the individuals engaged in various entrepreneurial activities. The ownership style, nature of enterprise, experience, educational qualifications and push and pull factors of the sample enterprises were considered as the main source of primary data.

In order to answer the research questions, a casual research was designed to identify the pull and push factors. The types of data used in the study include primary data, which was collected through a structured online-based survey questionnaire (see Appendix 1). The main purpose of the survey was to collect and analyze the data that help to examine the factors that influence individuals to become entrepreneurs. The questionnaire was framed using Google Form, as face-to-face survey was not possible due to Covid-19 pandemic. The questionnaire consists of five components: Title, basic information about the respondents, instructions about the level of agreement, list of push factors and list of pull factors. The respondents were asked to provide their level of agreement on a five-point scale ranging from strongly disagree (1) to strongly agree (5).

Selection of Push and Pull Factors: Based on the literature review and expert judgment, 11 pull factors and seven push factors were identified. The push and pull factors have been selected in such a way that there is no overlap between the factors. Hence, all the factors are independent and can be differentiated from each other. The number of push and pull factors listed in Table 1 meet the objective of the study by helping gather the necessary information required for further analysis.

Selection of Tool: For data analysis and selection of push and pull factors, the software used is Stata/IC 15.0 which is a general purpose statistical software package created in 1985 by Stata Corp.

The data analysis has been performed in two ways:

1. Average response of the respondents to a statement.
2. Principal Component Analysis (PCA) of the response.

Average response of the respondents to a statement is a general analysis which has been done by calculating arithmetic mean and standard deviation of the responses of the respondents on a scale of 1 to 5 for the identified factors. Arithmetic mean is the total of the sum of all values in a collection of numbers divided by the total of numbers in a collection. It is calculated in the following way:

Arithmetic Mean = X_1n = X₂+ + X_n Through PCA, the number of possibly correlated variables are transformed into a smaller number of variables called principal components by applying sophisticated underlying mathematical principles. PCA represents the direction in which the datasets are commonly spread out and also have maximum variance as well. In case of large data sets having several features, PCA plays a bigger role in finding out the variables which are most important.

The PCA used in this study for exemplification takes into consideration seven push factors 11 pull factors that measure an individual's intention to become an entrepreneur. For every factor, a numerical scale with five levels has been used (5-strongly agree and 1-strongly disagree). Assuming variables are collinear. The prime objective of doing PCA was to reduce the number of variables that measure an individual's intention to become an entrepreneur.

Firstly, Kaiser-Meyer-Olkin (KMO) test was performed to assess the suitability of each factor by indicating the proportion of variance in the factors. High values (close to 1.0) generally indicate that the analysis will give useful results with the availed data. If the value is less than 0.50, the results of the analysis may not be very useful.

Secondly, eigenvectors and eigenvalues of each factor were assessed as both are equally important for the analysis. The skeleton of PCA is built on the concept of eigenvectors and eigenvalues.

Eigenvectors represent directions. On the other hand, eigenvalues represent magnitude or importance. Bigger eigenvalues correlate with more important directions.

Thirdly, Varimax rotation of the factors, after assessing eigenvectors and eigenvalues, was performed. Varimax rotation is a rotation of the loading matrix after PCA. It maximizes the variance of the squared loading within factors by applying the orthogonal Varimax rotation (Kaiser, 1958).

Finally, based on the component loading derived from Varimax rotation, push and pull factors were identified based on eigen values. The result of PCA analysis was then compared with the outcome of average response of the respondent to a statement and finally the governing push and pull factor was selected.

Results and Discussion
From the demographic analysis in Table 2, it has been observed that among the 57 respondents, 72% are females and the remaining are males. The percentage also indicates and justifies the fact that more women are opting to become entrepreneurs than men. The biggest group is 20-25 years (47%), followed by 25-30 years (42%) and 30 and above (11%). This clearly indicates young people are choosing self-employment to become solvent. In terms of educational qualification, most of the respondents (86%) are either doing their graduation or have completed it. This shows the consistency with the age category of the respondents is less than 30 years. Maximum respondents are found to be having less than 5 years of experience (61%) in the field of entrepreneurship, followed by 21% who are found to have just started their entrepreneurship career. Only 18% have been found to be having 10 or more than 10 years of experience. 67% of the respondents are providing service (i.e., clothing, home

food delivery, jewelry, etc.), followed by trading (26%) and real estate (7%). In terms of ownership style, maximum respondents are found to be opting for sole-owner enterprise (49%), followed by partnership (35%) and very few are having a private company (16%).

The responses received are analyzed, and the results are presented in Appendix 2.

The result of the average response of the respondents to a statement for push factors in Table 3 shows that mean values of statements like 'Previous job stress', 'Previous job dissatisfaction', 'Insufficient salary' and 'Inflexible work schedule in the previous job' show that maximum respondents recorded their agreement level close to '3', which is equivalent to 'Neutral'. It should be mentioned that as many of the respondents were found to be having just completed their graduation or doing graduation, they might not be having any previous job experience. In such cases, for these four particular questions, a special condition was provided that if a respondent did not have any previous experience, they have to record their response as 3. So, the responses for these four questions are close to '3' (Table 3) which indicates that most of the respondents did not have any previous job experience. Hence, these factors cannot be a push factor logically.

The average responses against two factors, that is ,"Lack of higher formal education" (1.67) and "Family Hardship" (2.02), shows that the respondents do not find these two as push factors, as their responses do not support these two statements.

With the remaining one, 'Curse of unemployment', most of the respondents are in agreement (3.55), which is almost close to 4 (agree). Based on the mean value of all the statements, the study found the factor 'curse of unemployment' as the push factor which forces maximum respondents to become an entrepreneur.

In case of pull factors as shown in Table 4, the respondents show high level of disagreement with the statements like 'support from the government' and 'financial support from the bank' which indicates that not all the respondents are getting support from either government or the banks while trying to become an entrepreneur. So, as respondents disagree with these two statements and so they cannot be the pull factors logically.

On the contrary, respondents highly agree (average response close to 5) with the statements like 'strong will power to do something on own', 'self-employment and economic freedom', 'use of personal knowledge and previous experience', 'independence and flexibility and career advancement'. Among these, 'strong will power to do something on own' ranked first as the mean response level for this one is highest (4.47) among all responses. Hence, this has been found to be the pull factor among others with which the respondents agree.

From the KMO test of push factors, it is clearly visible that two push factors and two pull factors have KMO value below 0.5, which indicates that the results of the PCA analysis probably would not be very useful. Hence, these factors are deducted from the list and further KMO test was performed on the remaining variables after the reduction.

The Kaiser-Meyer-Olkin measure of sampling adequacy is a statistic that indicates the proportion of variance in the variables that might be caused by underlying factors. High values (close to 1.0) generally indicate that a factor analysis may be useful with the data. If the value is less than 0.50, the results of the factor analysis probably would not be very useful. Six factors had KMO value below 0.5, which indicates that the results of the PCA analysis probably would not be very useful. Hence, these six factors were removed from the list, and further KMO test was performed on the remaining variables. Finally, eight variables or factors based on KMO test came out as shown in Table 5.

Tables 6 and 7 show the eigenvalues for each component for the push and pull factors, the difference in eigenvalue size between the components, the proportion of variation explained by each component and the cumulative proportion explained. For example, for push factors, the first three components which have eigenvalue more than 1, already explain 66% of the variation in the data and for pull factors, the first two components which have eigenvalue more than 1, already explain 78% of the variation in the data.

An eigenvalue of 1 means that the principal component would explain one variable's worth of variability, and therefore, the eigenvalue criterion states that only components with eigenvalues greater than 1 should be retained. From Table 6, the first three components of push factors and from Table 7 the first two components of pull factors have eigenvalue of one or more than one, hence these components will be retained finally.

The results of Varimax rotation give output of component-wise loading on variables for both push factors and pull factors. From the component-wise loading on variables, the push and pull factors were identified.

Table 8 shows component 1 having higher loading on factors like 'Previous job stress', 'Previous job dissatisfaction', 'Insufficient salary in previous job' and 'Insufficient work schedule in previous job', which clearly indicates that component 1 is all about respondents' perception about previous job. But this cannot be counted as a push factor as maximum

respondents have been found to be having no previous experience; hence the loading is higher for these four statements.

On the other hand, 'curse of unemployment' has higher loading (0.88) on component 3. Also, maximum respondents have shown agreement with this statement. Hence, it can be said that 'curse of unemployment' is the governing push factor.

Table 9 shows component 1 having higher loading on factors like 'Earning extra money for family', 'Self-employment and economic freedom', 'Gaining Higher Social Status', 'Use of personal knowledge and previous experience', 'career advancement' and 'Strong will power to do something on own', which clearly indicates that component 1 is all about respondents' positive attitude towards entrepreneurship. But these cannot be counted as push factors as maximum respondents have been found to be having no previous experience; hence the loading is higher for the aforementioned statements.

On the other hand, 'Support from government' and 'Financial support from the Bank' have higher loading (0.69 and 0.68 respectively) on component 2. But these cannot be counted as pull factors as respondents have shown disagreement with these statements.

So, in component 1, the factors which have more loading close to 0.40 are 'Self-employment and economic freedom', 'Use of personal knowledge and previous experience', "Career advancement' and 'Strong will power to do something on own'. Finally, these four can be counted as the pull factors which encourage an individual to become an entrepreneur.

Based on the comparison between the outcome of 'Average response of the respondent on a statement for pull factors' and 'PCA of the responses of pull factors', one factor has been identified as push factor and four factors have been identified as pull factors.

The final list has been tabulated below (Tables 10 and 11), where the push and pull factors have been organized in a serial order based on the eigenvalue and percentage unexplained for PCA and based on arithmetic mean for average response of the respondents to a statement on pull factors. The findings show four factors act as pull factors which motivate people to become an entrepreneur. On the other hand, one factor acts as push factor which forces people to become an entrepreneur. The push factor supports the pull factors. The identified push factor is found to be 'curse of unemployment' which is a common scenario in Bangladesh. It supports the current unemployment scenario of Bangladesh. The latest survey of Bangladesh government has found 2.6 million unemployed people in the country. The unemployment rate marginally decreased from 4.3% in 2013 to 4.2% in 2019. The change is less with respect to creation of job opportunity during this period, but the

rate of youth unemployment has been increasing. In 2010, the youth unemployment rate was 6.4, whereas in 2020, it jumped to 11.56% (ILO estimate). This curse of unemployment has pushed many youngsters to become entrepreneurs. This leads to the validity of the identified pull factor.

The curse of unemployment may have created strong will power to do something on their own; also strong passion towards entrepreneurship may also create this will power in individuals to become an entrepreneur, to become self-employed and gain economic freedom. They want to use their personal knowledge and previous experience to create the base for financial freedom. Many people are found to believe that entrepreneurship will help in their career advancement.

Conclusion
The objective of this study was to identify the push and pull factors which influence an individual to become an entrepreneur.

In general, the factors contributing to the development of entrepreneurship, can be broadly divided into 'pull' and 'push' elements (Turner 1993; and Epstein 1993). Pull factors include all those reasons that emphasize entrepreneurship as a positive and desirable alternative and pull individuals to take up entrepreneurship. The pull or ambitious factors motivate the entrepreneurs to initiate ventures. It is needless to say that ambitions differ among individuals on the basis of their personal characteristics. Therefore, ambitions which nourish people's desire to achieve something could bring economic growth and development.

Limitations and Future Scope: These findings are of importance to entrepreneurs and policymakers. But the study has its own limitations. This study was conducted based on the opinions of those entrepreneurs who are living and doing business in Dhaka City. Also, their opinions cannot be generalized. Further research can be conducted focusing on the impact of these factors on the success of entrepreneurs at a larger scale.

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