Motivations for Engaging in Entrepreneurial Activity in the Informal Sector in Sub Saharan Africa

Transcription

1 Motivations for Engaging in Entrepreneurial Activity in the Informal Sector in Sub Saharan Africa Master s Thesis 15 credits Department of Business Studies Uppsala University Spring Semester of 2018 Date of Submission: Alexander Beyer Blake Morgan Supervisor: Philip Kappen, PhD

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3 Table of Contents Abstract Introduction Literature Review Opportunity- and Necessity-Motivated Entrepreneurship Gender Entrepreneurial Parents Human Capital Education Sector Experience General Experience Serial Entrepreneurial Experience Methodology Dataset Empirical Model Data Preparation Variables Dependent Variable Primary Independent Variables of Interest Control Variables Results Descriptive Statistics Regression Results Discussion Conclusion References

4 Abstract In this paper we investigate entrepreneurship in the informal sector in Sub-Saharan Africa. Using data from the World Bank we examine the motivational antecedents for why individuals become self-employed within the informal sector. We build on research focusing primarily on data from the formal sector to generate a number of testable hypotheses regarding individual-level predictors of opportunity status. We test our hypotheses using multiple probit model regression analyses. Our results indicate that opportunity-driven entrepreneurs comprise a large portion of informal sector in Sub-Saharan Africa and suggest that there are important differences between the antecedents of entrepreneurship in the informal sector in the region and the findings of research focused on the formal sectors of developed countries. Despite a number of limitations, our paper sheds important light on an interesting and comparatively understudied topic but leaves much room for future development. Keywords: Opportunity entrepreneurship; Necessity entrepreneurship; Informal sector; Entrepreneurial antecedents; Determinants of entrepreneurship; Sub-Saharan Africa 1. Introduction Our thesis seeks to investigate motivations for engaging in entrepreneurial activity in the informal sector in Sub-Saharan Africa. Exploring the motivations for why entrepreneurs engage in entrepreneurial activity is important, not only because doing so promises to deepen the scholarly understanding of entrepreneurship, but also because entrepreneurs that are driven by different motivations may have differing impacts on economic development (Acs, Desai, and Klapper, 2008). We examine the informal sector in particular because of its importance, both in terms of percentage of total employment and share of gross domestic product, in most developing countries. The informal sector in Sub-Saharan Africa is the world s largest, with weighted yearly data from showing that it accounted for 36.5 percent of the region s gross domestic product (Schneider, Buehn, and Montenegro, 2010). Even more striking is the fact that the informal sector accounts for between 50 and 75 percent of employment in the region (Sparks and Barnett, 2010). Furthermore, research suggests that the informal sector may have had an outsized role in economic growth in recent decades in many Sub-Saharan economies 3

5 (Verick, 2008). Given its importance and size, the informal sector is a necessary component in discussions about entrepreneurship, job creation, and poverty-reduction in the region. Since the early 2000 s, it has been common for researchers to categorize entrepreneurs according to the primary motivation for why an individual decides to enter self-employment. This categorization has revolved around a distinction between necessity-driven entrepreneurs, who are pushed into self-employment by the lack of other viable alternatives, and opportunitydriven entrepreneurs who enter self-employment because they identify and pursue favorable opportunities in the marketplace (Sahasranamam and Sud, 2016). Research focused on the formal sector shows that necessity- and opportunity-motivated entrepreneurship have different effects on economic growth. While necessity entrepreneurship is thought to contribute minimally to economic development, opportunity entrepreneurship is considered to drive technological change and economic advancement (Wennekers et al., 2005). Formal-sector-focused studies have also examined the levels of opportunity and necessity entrepreneurship across countries, the aggregate- and individual-level antecedents of each category of entrepreneurship, and the individual- and firm-level characteristics that are associated with each type of entrepreneur (Block, Sandner, and Spiegel, 2015). In contrast to the formal sector, entrepreneurs motivations in the informal sector are understudied. Most recent studies either focus exclusively on formal sector entrepreneurs, by only looking at registered firms, or do not clearly distinguish between the formal and informal sectors. Given the relative scarcity of research focused on entrepreneurs motivations in the informal sector, there are many gaps in the literature on the subject. According to a traditional view, individuals engage in the informal sector due to their inability to enter the formal sector. The existence of opportunity-driven entrepreneurs in the informal sector, however, refutes this generalization (Webb et al., 2013; Gibbs, Mahone, and Crump, 2014; Hallam and Zanella, 2017). Overall, there is much scope for future research to build on the formal-sector-focused literature using informal-sector data. Our thesis aims to begin to fill the gaps in the existing literature by building on research on opportunity- and necessity-based motivations for engaging in entrepreneurial activity in the formal sector by investigating the distinctive characteristics of opportunity- and necessity-driven entrepreneurs in the informal sector. The research question that this paper is built around is: 4

6 What business- and individual-level characteristics are associated with opportunity-driven entrepreneurship in the informal sector in Sub-Saharan Africa? By seeking to answer this question, our thesis attempts to develop a preliminary understanding of motivations for entrepreneurial activity within the informal sector in Sub- Saharan Africa and to show how the determinants of entrepreneurial motivations differ between the formal and informal sectors. The data for our empirical analysis is drawn from the World Bank s Informal Enterprise Surveys conducted in the Democratic Republic of Congo (DRC), Ghana, and Kenya in 2013 (World Bank, 2014). The dataset allows us to identify entrepreneurs and categorize them as either opportunity- or necessity-driven. Our paper uses a probit model regression analysis to test hypotheses related to the firm- and individual-level characteristics that are associated with each category of entrepreneurship. We believe that contributing to the research on entrepreneurial motivations in the informal sector will help enable policy makers to increase their understanding of why individuals choose to enter the informal economy and thereby better inform their policy decisions. Section One of the paper presents an introduction. Section Two gives an overview of the existing literature on the motivations for engaging in entrepreneurial activity with a particular focus on the categories of opportunity- and necessity-driven entrepreneurship and elaborates on our theoretical arguments and testable hypotheses. Section Three discusses the data, our specified model, and variables. Section Four will presents the results, and Section Five discusses the results and validity of our analysis. Finally, Section Six presents the conclusion. 2. Literature Review 2.1 Opportunity- and Necessity-Motivated Entrepreneurship It is widely accepted that entrepreneurship describes the discovery, evaluation, and exploitation of opportunities in the marketplace (Shane and Venkataraman, 2000). Furthermore, it is thought that individuals who engage in entrepreneurship create future goods and services and contribute to economic growth through innovation and technological change (Schumpeter, 1934; Shane and Venkataraman, 2000). Although some argue that not all inventions lead to economic growth and that entrepreneurs advance the economy only once market opportunities 5

7 have been identified (Shane, 2002). Differences in individuals perceptions of available opportunities and their potential payoffs determine whether opportunities are exploited or not (Amit, Muller, and Cockburn, 1995). The traditional view of entrepreneurship as exploring and exploiting new business opportunities has recently been nuanced with the categorization of entrepreneurs as opportunityand necessity-driven. These different motivations for why individuals decide to enter selfemployment were first described by Gilad and Levine (1986). These researchers described a push-pull theory of entrepreneurship, whereby an individual is either pushed into entrepreneurship by negative situational factors, such as loss or lack of employment, or pulled into self-employment because the individual recognizes and aims to exploit a potentially profitable opportunity in the marketplace (Gilad and Levine, 1986). Starting in 2001 the Global Entrepreneurship Monitor (GEM) has included corresponding categories with its empirical data and researchers began to classify pushed individuals as necessity-driven and pulled individuals as opportunity-driven entrepreneurs (Reynolds et al., 2001). Since its inception in 1999, the GEM has surveyed adults in over 100 economies to gauge entrepreneurial behavior and attitudes of individuals and the national context and how that impacts entrepreneurship (Gemconsortium.org, 2018). During the years 1999 and 2000, the GEM did not actively distinguish between why individuals choose to start their own ventures. From the year 2001, answers to survey questions such as Are you involved in this start-up to take advantage of a business opportunity or because you have no better choices for work? created the categorization of entrepreneurs into opportunity- or necessity-driven types (Reynolds et al., 2002). According to Reynolds et al. and GEM (2005), and similar to the theory published by Gilad and Levine, individuals are either pulled or pushed into entrepreneurship (McMullen et al., 2008; Sahasranamam and Sud, 2016). Necessity-driven entrepreneurs enter self-employment due to the need to survive and lack other of employment options. In contrast, opportunity-driven entrepreneurs start new ventures, despite other viable options, to explore and exploit business opportunities (Valdez and Richardson, 2013). Opportunity entrepreneurship resembles the classical notion of an individual discovering market opportunities through previous experience and knowledge (Kirzner, 1997). 6

8 Since these two categories became popularized with their inclusion in the GEM, several studies have examined the characteristics of opportunity- and necessity-entrepreneurs in low-, medium-, and high-income countries to gain a better understanding of the aggregate levels of each (Block and Wagner, 2010; Brünjes and Diez, 2013; Block et al., 2015; Sahasranamam and Sud, 2016). While there may be overlap and potential inconsistencies for individuals answers to questions regarding their reasons for why they are self-employed, we feel these categories are relevant because they entail characteristics such as perceived opportunity costs. Recently some researchers have begun pushing back against the binary categorization of entrepreneurial motivations into opportunity- and necessity-driven categories. Authors such as Williams (2008) and Gibbs et al. (2014) have discussed empirical results that indicate that entrepreneurial motivations are more appropriately depicted along a continuum rather than neatly fitting into two categories. These authors also point out that opportunity- and necessity-related factors are often found to concurrently play a role in the decision of many entrepreneurs to start a business. We believe that a more nuanced view of entrepreneurial motivations is important and also that accepting the idea that opportunity and necessity often simultaneously influence the decision to engage in entrepreneurial activity is necessary for better understanding entrepreneurial motivations. However, we argue that distinguishing between the categories of opportunity- and necessity-driven entrepreneurship is still informative and important. Even if opportunity- and necessity-related factors figure simultaneously, entrepreneurs can be divided into two categories based upon opportunity costs. Those that fall into the opportunity category have other viable alternative income sources that they chose to forgo in order to pursue a business opportunity. Therefore, they have relatively higher opportunity costs. Conversely, entrepreneurs that fall into the necessity category do not have alternative income sources and thus do not forgo other opportunities to start their own businesses. The informal economy in general is defined as generating income through illegal activities, which take place outside of formal institutional boundaries such as tax laws and regulations (Castells and Portes, 1989). According to De Soto (1989), these activities are not antisocial in intent but are considered to be within societal norms, such as values and beliefs (Sahasranamam and Sud, 2016). The informal sector contributes up to 70 percent of GDP in some developing countries in places like Sub-Saharan Africa, which is the focus of this paper (Schneider and Enste, 2002). 7

9 While there is a rather large body of scientific research and empirical data on entrepreneurship in the formal sector, as outlined by this literature review, only a limited number of studies have been carried out that look specifically at informal economies (Bhola et al., 2006; Block and Wagner, 2010; Kelley, Singer and Herrington, 2016). Empirical data available from sources such as the GEM and World Bank Group Entrepreneurship Survey (WBGES) indicate different rates of entrepreneurial activity across countries. A study carried out by Acs et al. (2008) comparing data gathered from these two sources found that the WBGES reports higher rates of entrepreneurship in developed countries in comparison to the GEM, which found more entrepreneurial activity in developing economies. The researchers account for these differences in the GEM data and how it captures the presence of informal economies in developing countries. Moreover, they argue that entrepreneurs in developed countries have greater incentives and ease to incorporate their business activities into the formal sector than those in developing countries (ibid). Earlier work done by Castelles and Portes (1989) and other studies argue that entrepreneurs enter the informal sector out of necessity. This picture has changed in recent years. Further research into individuals motivations indicate that entrepreneurs see economic opportunities when entering the informal sector, such as more perceived flexibility, autonomy, and freedom from regulations and corruption often found in developing economies (Gërxhani, 2004; Dutta, Kar, and Roy, 2013). For the informal sector Webb et al. (2013) found that institutional, motivation-related, and resource allocation effects differ from entrepreneurs in the formal sector. The study mentions factors such as weak law enforcement and distrust in government, which encourage individuals to start a venture in the informal rather than in the formal sector. Additionally, recent work by Gibbs et al. (2014) argue that individuals entering the informal sector can be both necessity- or opportunity-driven. Furthermore, studies of individuals and their decisions to enter the informal economy in Bolivia, Uganda, England, Ukraine, and Russia question the binary necessity/opportunity entrepreneurship model and argue that there may likely exist a continuum along which entrepreneurs can be classified showing that entrepreneurs can have mixed motivations starting out of necessity and becoming opportunity-driven entrepreneurs later on in their business development (Williams, 2007; Williams, 2008; Langevang, Namatovu, and Dawa 2012; Gibbs et al., 2014; Hallam and Zanella, 2017). The studies in question are limited by the fact that they 8

10 employ relatively small datasets and often rely on entrepreneurs own perceptions of whether they are opportunity- or necessity-driven rather than more objective measures, such as whether they held a job before deciding to engage in entrepreneurship. Scholars in the field of entrepreneurial research report that there are significant differences between opportunity- and necessity-entrepreneurs at the individual level (Amit et al., 1995; Block and Wagner, 2010). Antecedents for the two different types of entrepreneurship are wide ranging and include gender, age, level of education, risk aversion, social and human capital, access to financial capital, self-employed parents, complexity of administrative procedures, level of corruption, and general economic climate (Reynolds et al., 2002; Reynolds, Bygrave, and Autio, 2003; Arenius and Minniti, 2005; Van Stel and Stunnenberg, 2006; Block et al., 2015). Bygrave, Hay, Ng, & Reynolds (2003) found association between opportunity entrepreneurship and informal investment, entrepreneurial capacity, and perception of start-up opportunities. Conversely they found that necessity entrepreneurship did not show significant correlations with these variables. Today there is only limited research available on the individual-level motivations for why individuals in the informal sector choose to start their own ventures. In this study we examine empirical data from the informal sector to increase the understanding of individual motivations for entering self-employment in the informal sector and whether individuals can be classified as either opportunity- or necessity-driven entrepreneurs. The individual-level characteristics that form our hypotheses regarding the antecedents of opportunity entrepreneurship have been used in previous studies focusing on the formal sector. The characteristics are gender, entrepreneurial parents, level of education, sector experience, general experience in terms of age, and serial entrepreneurship. 2.2 Gender In the Women s Entrepreneurship 2016/2017 Report the GEM consortium summarizes that worldwide women in general are 20 percent more likely to start businesses out of necessity than men (Kelley et al., 2017). Another GEM report showing data collected from 60 countries worldwide in the years 2015 and 2016 indicates that in innovation-driven economies, such as many European countries, as well as the USA and Canada, female entrepreneurs represent around only 3 to 6 percent of the total female population (Kelley et al., 2016). 9

11 Additionally, the report shows that in efficiency- and factor-driven economies- such as countries in Latin American and the Sub-Saharan Africa region- 13 and over 30 percent respectively of all women engage in entrepreneurial activity (Kelley et al., 2016; Kelley et al., 2017). While it is difficult to interpret the available data in terms of differences in numbers of women working in the informal or formal sector one can assume that in developed countries most entrepreneurial women have registered businesses, whereas in developing countries the informal sector may account for a large percentage of entrepreneurs. Researchers have not found a clear root cause as to why women are less likely to engage in entrepreneurship compared to men. Minniti (2010) looked at female entrepreneurs in over 34 countries ranging from low to high GDP per capita. In her data she shows that the gender gap widens as GDP increases and fewer women enter self-employment. Her analysis supports previous research in showing that women in developing countries show higher propensities to engage in entrepreneurship, which is often linked to the need to manage time, location, and family commitments. While overall entrepreneurial activity is negatively associated with GDP, for women specifically research suggests that factors such as better employment options, more developed childcare and social systems, overall preference for self-employment, as well as subjective perceptions about personal skills, education and potential failure, are connected to fewer entrepreneurial women in higher income countries (Minniti, 2010; Verheul et al., 2010; Kelley et al., 2017). The ratios between female and male opportunity entrepreneurs in both reports indicate on average that men are more likely to exploit business opportunities than women (Kelley et al., 2016; Kelley et al., 2017). Similarly, research done by Block and Sandner (2009) in Germany shows that women are as less likely to be opportunity entrepreneurs than men. Based on the research found on gender s effect on necessity and opportunity entrepreneurship we formulated the following hypothesis: H1: The gender hypothesis Female entrepreneurs in the informal sector are less likely to be opportunity entrepreneurs than male entrepreneurs. 10

12 2.3 Entrepreneurial Parents The positive effect of entrepreneurial parents on their children to become entrepreneurs has widely been accepted. Hundley (2006) analyzed two empirical datasets ranging from the years 1972 to In his research, his conclusions that individuals with self-employed parents are more likely to enter self-employment are in agreement with previous studies such as work done by Waddell (1983), Cooper and Dunkelberg (1986), Dunn and Holtz-Eakin (2000), and others. Moreover, he argues that apart from the entrepreneurial capital, which children acquire from their self-employed parents, there are additional factors such as parental economic background. Lindquist, Sol, and Van Praag (2013), studying a large Swedish sample, also found concurring evidence that children with entrepreneurial parents are 60 percent more likely to become entrepreneurs compared to children without self-employed parents. A small number of studies has examined the connection between individuals motivations for the decision to engage in entrepreneurial activity and having entrepreneurial parents (Wagner, 2005; Verheul et al., 2010). These researchers differentiated individuals into necessity or opportunity entrepreneurs, which are either pulled or pushed into self-employment. In Verheul s study, 2007 survey data from 27 European countries and the USA was analyzed to find motivational differences between necessity and opportunity entrepreneurs. Their data shows that having at least one entrepreneurial parent increases the probability of becoming an opportunity-driven rather than necessity-driven entrepreneur (Verheul et al., 2010). Wagner (2005) analyzed Regional Entrepreneurship Monitor (REM) survey data for 12,000 persons for the years 2000 to 2003 in Germany and found that nascent opportunity entrepreneurs are more likely than necessity entrepreneurs to have entrepreneurial role models in their families. Furthermore, Djankov (2005) reported that in Russia family networks have a positive effect on engaging in opportunity entrepreneurship but only marginally so for necessity entrepreneurship. Based on this research, one would expect that having entrepreneurial parents increases the probability of becoming an opportunity entrepreneur rather than a necessity entrepreneur. Therefore, we hypothesize: H2: The exposure hypothesis Informal-sector entrepreneurs with parents who are or were entrepreneurs are more likely to be opportunity entrepreneurs than those without entrepreneurial parents. 11

13 2.4 Human Capital Education Research indicates that having a higher level of education increases the likelihood of engaging in opportunity-driven entrepreneurship rather than entering self-employment out of necessity (Reynolds et al., 2003). It can be argued that opportunity-driven entrepreneurs fit the picture of individuals being able to identify and exploit business opportunities in the marketplace that is described by Shane (2000). The ability to identify these opportunities, Shane argues, is based on prior industry experience and education. In line with studies by Shane (2000) and Shane and Venkataraman (2000), Levie and Autio (2008) found that higher education, and in their case even post-secondary entrepreneurship-related education, has a positive and significant association with the ability to recognize market opportunities. Interestingly, the study finds only a weak association of post-secondary entrepreneurship education and start-up skills perception, leading the authors to speculate that these findings could be due to a lack of practical education or that starting a business requires a too-general skill set, making it difficult to teach in class. Conversely, studies by Ardagna and Lusard (2008) and Poschke (2013) that analyze GEM micro-level data from over 150,000 individuals in over 37 countries over several years conclude that necessity entrepreneurs tend to be less educated than opportunity entrepreneurs. Poschke (2013) notes that individuals with 12 or more years of schooling show a negative and significant probability of being necessity entrepreneurs across all countries included in the survey data Sector Experience Previous experience leads entrepreneurs to better recognize opportunities (Shane and Venkataraman, 2000). Looking at labor market experience in particular, researchers agree that having worked in the industry in which one s new venture operates in is significantly associated with opportunity entrepreneurship (Davidsson and Honig, 2003; Block and Wagner, 2010; Baptista, Karaöz and Mendonça, 2014). In a study examining the US labor market by analysing data from 20,000 households for the time period 1983 to 1986, Bates (1995) reports that industry 12

14 specific work experience leads to increased entrepreneurial engagement levels. Another US study surveying 1,547 business owners over the years 1985 to 1987 carried out by Gimeno et al. (1997) looked at venture performance and likelihood of exit. The researchers found that specific human capital, which translates to sector experience leads, to greater venture performance, but has a strong negative relationship to exit. They argue that individuals with specific knowledge of their industry are less likely to exit due to better venture performance and that their specific knowledge may not be of the same value in a different industry General Experience Ucbasaran, Westhead, and Wright (2003) and Davidsson and Honig (2003) published work looking at general and specific human capital. They argue that education counts as general human capital because it can be codified and is broadly available. More specific human capital based on experience,such as business owner experience from previous ventures and knowhow, is equally if not more important in recognizing and pursuing market opportunities (Ucbasaran et al., 2003). Their results show that although individuals with more entrepreneurship-specific human capital pursue more opportunities and that therefore, general human capital has an important role in the identification and pursuit of market opportunities (ibid). In a later study, the same authors confirmed the importance of entrepreneurship-specific human capital and a higher probability of identifying and exploiting opportunities (Ucbasaran, Westhead, and Wright, 2008). Moreover, the authors found that higher levels of previous business ownership experience, managerial, and entrepreneurial capabilities lead to a greater ability to convert an idea into an opportunity (ibid). Gaglio and Katz (2001) argue in their Entrepreneurial Alertness Model, which refines Kirzner s theory (1979), that these types of capabilities are needed for the entrepreneur to see a means-ends relationship between idea and opportunity. Age can be seen a proxy for experience. One would expect that greater age is associated with more experience and thereby leads to greater ability to recognize and pursue opportunities. Therefore age may be assumed to be positively associated with an increased probability of being an opportunity entrepreneur. 13

15 Interestingly, Block and Sandner (2009) found in one of two probit model analyses of over 600 self-employed German individuals over the years 1990 to 2003 that higher age has a significantly negative influence on being an opportunity entrepreneur. However, their second model shows no significant association of higher age on the probability of being an opportunity entrepreneur. We posit that the result from the first model in Block and Sander (2009) is specific for the German context and despite it argue that age, as a proxy for general experience, and opportunity entrepreneurship should be positively associated. From the above categories, which are all related to human capital, we develop the following hypotheses: H3: The human-capital hypothesis Informal-sector entrepreneurs with higher levels of human capital are more likely to be opportunity entrepreneurs. Human-capital hypothesis is split into three sub-hypotheses. H3a: The education hypothesis Informal-sector entrepreneurs with higher education levels are more likely to be opportunity entrepreneurs. H3b: The sector-experience hypothesis Informal sector entrepreneurs with more years of experience in their business sector are more likely to be opportunity entrepreneurs. H3c: The general-experience hypothesis Older informal sector entrepreneurs are more likely to be opportunity entrepreneurs. 2.5 Serial Entrepreneurial Experience Ardichvilia, Cardozob, and Ray (2000) analyzed opportunity alertness antecedents in entrepreneurs. In contrast to other researchers, the authors argue that (at the time of their publication) there exists no comprehensive theory of opportunity identification and development. They further state: Such a theory is critical if we want to successfully bridge research and practice: a sound theory provides a means of identifying and defining applied problems; it provides a means of prescribing or evaluating solutions to applied problems; and it provides a means of responding to new problems that have no previously identified solutions. (ibid) 14

16 In their paper the researchers focused on serial entrepreneurs, those individuals who have experience creating multiple businesses, and formalized a theory to predict how experience from previous venture creation, among other factors, affects future opportunity recognition. They suggest a set of propositions, which include prior knowledge in areas such as markets, customer problems and ways to serve markets, as factors that increase the likelihood of successful entrepreneurial opportunity recognition (ibid). An interesting study by Westhead, Ucbasaran, and Wright (2005) examined 2,900 Scottish firms through a survey in the year 2000 and split the respondents into novice, serial, and portfolio entrepreneurs. The authors indicate that while serial entrepreneurs are more likely to recognize more business opportunities in comparison to novices, they are more cautious than portfolio entrepreneurs in exploiting new opportunities and likely favor a repetition of previous successes in forming a similar business (ibid). Another study performed by Baron and Ensley (2006) draws from models within the cognitive science research field and analyzed 194 US entrepreneurs divided into 88 experienced and 106 novice entrepreneurs. In asking four open-ended questions, the authors derived different variables to examine individuals' cognitive frameworks for pattern recognition. Their results show that experienced entrepreneurs have greater cognitive capabilities in recognizing and exploiting new business opportunities compared to novices. In summary, one can conclude that serial entrepreneurs have gained the experience to better recognize future business opportunities. While necessity entrepreneurs may need to change businesses due to unforeseen shifts in demand or other factors, it is reasonable to believe that opportunity entrepreneurs are more likely to actively exit one business and pursue a new one due to their ability to recognize and exploit opportunities. To examine the effect of serial entrepreneurial experience on opportunity entrepreneurship in the informal sector we examine the following hypothesis: H4: The serial-entrepreneur hypothesis Informal-sector entrepreneurs that started multiple businesses in the recent past are more likely to be opportunity entrepreneurs. 15

17 While the cited research often does not specifically distinguish between formal and informal economies, when not otherwise specified the great majority of the data presented originates from registered ventures and from medium- to high-income countries where informal economies are very small. 3. Methodology 3.1 Dataset This section describes the data and specified model employed in our empirical analysis. The data used in our study is from the World Bank s Informal Enterprise Surveys conducted in the Democratic Republic of Congo (DRC), Ghana, and Kenya in 2013 (World Bank, 2014). The Informal Enterprise Surveys (IFS) collect data on non-registered business activities and provides extensive information about businesses in the informal sector. The data from the IFS can be downloaded from the World Bank website once an electronic request for access has been granted. The dataset used in this paper includes 1,742 informal businesses in total with 480, 729, and 533 from the DRC, Ghana, and Kenya respectively. The use of these three countries is based upon the availability of quality data for the informal sector. The IFS provide detailed data on an extensive number of firm and entrepreneur characteristics (World Bank, 2014). The IFS are conducted in various regions across the world and are implemented in parallel to the World Bank s Enterprise Surveys (ES). The IFS employ a standardized survey instrument, which is designed to gather information on the business environment for nonregistered businesses. The surveys use a uniform sampling methodology so as to minimize measurement error and yield comparable data. The goal of the IFS is to provide information about the condition of the business climate for informal businesses, produce information about the reasons for informality, gather data for research on informality, and assess the level of informal-sector activity in select urban centers in chosen countries (World Bank, 2014). The IFS primary sampling units are non-registered business entities. The IFS conduct a screening procedure at the beginning of each survey to identify eligible interviewees. After this is done, the IFS gather full descriptions of all of the business owners or managers business and sorts the business into either manufacturing or services categories. It is important to note that 16

18 certain non-registered activities fall outside of the IFS definition of the informal sector. The IFS exclude strictly illicit activities, such as prostitution, racketeering, and drug trafficking. Additionally, the IFS also exclude individual activities that are variants of selling personal labor, such as domestic servants and windshield washers. IFS equate informality with non-registration, so the units found in the IFS are non-registered businesses that engage in otherwise legal operations. Registrations is defined according to laws of the country in which the sample is conducted. The DRC, Ghana, and Kenya define registration in accordance with accepted international norms (World Bank, 2014). The IFS conduct interviews in selected urban centers. The total number of interviews is predetermined and distributed based upon criteria such as each urban center s population and level of business activity. Prior to conducting the survey, the IFS divide each urban center into a number of zones, which are identified based on prior locally-collected information regarding the concentration of informal business activity and regional considerations (World Bank, 2014). To provide extensive information on the ranging aspects of the informal sector, the IFS samples are designed to have an approximately equal weight of service and manufacturing businesses. The classification into each respective category is based on questions regarding the business primary activity that are found in the screening portion of the questionnaire. In order to be included in the sample, service-based businesses must represent an ongoing business enterprise that goes beyond the personal sale of manual labor. For a business to be classified as belonging to the manufacturing category, it must require inputs and/or intermediate goods. Examples of manufacturing activities found in the sample are the production of furniture, the reprocessing of scrap metal, and processing of coffee, sugar, oil, and dried fruit. Examples of service activities found in the sample are dry-cleaning, the sale of foodstuffs, and computer repair service (World Bank, 2014). The strength of the dataset used in this paper is that it provides rich data on an understudied sector of the economy. The dataset provides information on a wide range of individual- and firm-level characteristics, including information on profitability. With questions related to entrepreneurship, the IFS are well-suited to research on entrepreneurship within the informal sector. However, the dataset does have some important inherent weaknesses. First and foremost, the sample of informal business is not representative of the informal sectors in the countries in question as a whole. Based upon how the surveys were conducted, the sample is 17

19 somewhat representative of informal businesses in urban centers but consciously under samples informal businesses operating outside of urban centers. This concern is somewhat mitigated by the fact that, according to Schneider et al. (2010), in Sub-Saharan Africa informal businesses outside of primary agriculture are concentrated in urban areas. Furthermore, the sample does not necessarily provide a representative breakdown between manufacturing- and service-focused business, since IFS seek to include a balance of businesses belonging to these categories in their samples. Therefore, while the dataset provides rich and informative data on the informal sectors in the countries in question and is representative to some degree, it cannot be taken to be representative of the informal sectors in these countries as a whole. 3.2 Empirical Model The dataset allows us to categorize informal sector entrepreneurs as either opportunity- or necessity-driven. Our thesis will use probit model regression analysis to test hypotheses related to the entrepreneur characteristics that are correlated with each category of entrepreneurship. Probit model regression analysis is consistent with the literature on the characteristics of opportunity- and necessity-driven entrepreneurs. Because probit model regression coefficients do not have an intuitive interpretation, we will interpret the elasticity of the relationships by calculating the marginal effects by taking the derivatives. Marginal effects present the change in probability for an infinitesimal change in each independent, continuous variable. For dummy variables the marginal effects report the discrete change in the probability for the dummy. This study uses a probit model regression analysis to test our four core hypotheses. Our specified model is as follows: P i = bv i +fw i +s X i +hy i +nz i +h i Where P i is the probability that firm i run by an opportunity-driven entrepreneur, V i indicates whether the entrepreneur is female,w i reflects whether the entrepreneur s parents are or were entrepreneurs, X i indicates the entrepreneur s level of human capital, Y i reflects whether the entrepreneur is a serial entrepreneur or not, Z i is a vector of control variables, and h i is the unobserved error term. 3.3 Data Preparation 18

20 We conducted the data analysis using the data analysis and statistical software program, Stata. In order to prepare the data for analysis a degree of cleaning had to be done. The respective datasets from each country that we downloaded from the World Bank website had to be appended and missing values had to be dealt with. Additionally, we combed through the data to identify potential outliers and irregularities. Since the data were in the form of questionnaire answers, our dataset contained both text and numerical answers in the form of binary, categorical, and continuous variables. Many of the variables used in our analysis had to be constructed, often from the responses to multiple questions in the questionnaire. 3.4 Variables The following subsection provides information about the variables used in our analysis and how they are specified. In order to simplify the interpretation of our results, all of the primary independent variables of interest were constructed as dummy variables in addition to their original format Dependent Variable Opportunity Entrepreneur: The dependent variable in our model is measured by Opportunity, a dummy variable that indicates whether the owner of a firm is an opportunitydriven entrepreneur. Opportunity takes the value of one if the business owner/primary decision makers responded that they were employed in the same activity as the current business, employed in a different activity, or self-employed in a different type of activity in response to the question: Prior to starting this business, what was the previous occupation of the largest owner? If the business owner/primary decision makers responded that they were Selfemployed in the same type of activity, unemployed, or Other (for example housewife, student, ect.), then they were categorized as a necessity entrepreneur. The reason that we decided to categorize those who said that they had previously been self-employed in the same type of activity as necessity entrepreneurs is that the answer to the question indicates that they did not give up an alternative income stream in order pursue their current business opportunity. Our definition of opportunity-driven entrepreneurship is in line with that GEM, which is generally used throughout the literature on opportunity- and necessity-driven entrepreneurship. Opportunity entrepreneurs are those who scarifice alternative options to pursue a business 19

21 opportunity, while necessity entrepreneurs pursue entrepreneurship primarily because they have no alternative source of work (Reynolds et al., 2002) Primary Independent Variables of Interest Female Entrepreneur: Female is a dummy variable that measures whether the entrepreneur is female. This dummy variable takes the value of one if a firm reports that its owner/primary decision maker is female. It has been found in research based primarily on the formal sector that women are more likely to be self-employed out of necessity and less likely to be opportunity entrepreneurs (Block and Sandner, 2009; Kelley et al., 2017). Female is used to test the gender hypothesis to examine if formal sector observations can be confirmed in the informal economy. Exposure to Entrepreneurship: Exposure is a dummy variable that indicates whether the entrepreneur s parents are or were entrepreneurs. This dummy variable takes the value of one if a business reports that its entrepreneur s parents own or have owned a business. When looking at the effect entrepreneurial parents have on the likelihood of their children becoming either necessity or opportunity-driven entrepreneurs, research indicates a stronger association with opportunity entrepreneurship (Wagner, 2005; Verheul et al., 2010; Lindquist et al., 2013). Exposure is used to test the exposure hypothesis. Education: Education is a categorical variable that represents the owner s highest level of education. The variable contains five categories: No education, primary school, secondary school, vocational training, and university. Abovesecondary is a dummy variable constructed from education that takes the value of one if a business reports that its owner/primary decision maker has vocational training or university as their highest education level. From our literature review, we can conclude that with more years of education the likelihood of being a necessity entrepreneur decreases (Poschke, 2013). Higher levels of education lead individuals to recognize more opportunities (Levie and Autio, 2008). Education and abovesecondary are used to test the education sub-hypothesis of the human-capital hypothesis. General Experience: Age is an ordinal variable that represents the age of the owner. Old is a dummy variable constructed from age that takes the value of one if the owner/primary decision maker is older than average for the dataset. Age serves as a proxy for general experience. Furthermore, research indicates that age plays a role when looking at individuals entrepreneurial activity (Block and Sandner, 2009; Kelley et al., 2016). Based upon 20

22 the literature we argue that with increased age the ability to recognize and exploit opportunities increases. Age and old are used to test the general-experience sub-hypothesis of the humancapital hypothesis. Sector Experience: Experience is a discrete variable that represents the number of years experience that the entrepreneur has in the business sector. Highexperience is a dummy variable constructed from experience that takes the value of one if the owner/primary decision maker has more years of experience in their business sector than the average for the dataset. Sector experience is associated with a better understanding of the specific industry, which in turn is thought to lead to increased opportunity recognition and opportunity-driven engagement levels (Bates, 1995; Baptista et al., 2014). Experience and highexperience are used to test the experience sub-hypothesis of the human-capital hypothesis. Serial Entrepreneur: Serial is a dummy variable that takes the value of one if the owner/primary decision maker started multiple businesses within the last three years. According to the theory created by Ardichvilia et al. (2000) serial entrepreneurs prior knowledge leads to increased opportunity recognition capabilities. We argue that a greater potential to recognize business opportunities leads individuals to become opportunity- rather than necessity-driven entrepreneurs. Serial is used to test the serial-entrepreneur hypothesis Control Variables One potential issue with our method is endogeneity. An edogeneitey problem occurs when there is a correlation between independent variables and the error term. For example, consider the following hypothetical case, which is an example of omitted variable bias, one particular cause of endogeneity. A hypothetical model for explaining opportunity status based upon individual characteristics includes gender as a primary independent variable of interest. Female entrepreneurs are underrepresented in large cities due to safety concerns for female business owners in such environments. Opportunity entrepreneurs tend to cluster in large cities. If this hypothetical model does not include a variable to control for the effect of being in large city then the results may falsely indicate that female entrepreneurs are less likely to be opportunity-driven. Therefore, in order to address potential endogeneity issues and omitted variable bias in particular, we include in our model a number of important control variables that may plausibly be associated with opportunity status. Our discussion section will address potential endogeneity concerns in greater detail. Our control variables are as follows: 21

23 Firm Size: size is a categorial variable that represents a firm s size based upon the total number of workers. There are three ascending categories: Individual, Small for firms with between two and five workers, and Large for firms with six or more workers. We expect size to be positively associated with opportunity entrepreneurship. Size at Startup: Startupsize is an ordinal variable that total number of employees at startup. We expect startupsize to be positively associated with opportunity entrepreneurship. Sales Size: Sales is a categorical variable that splits firms total sales into quintiles. It serves as a control for firm size. Sales are converted from local currency units into US dollars using historical exchange-rate data. We expect sales to be positively associated with opportunity entrepreneurship. Profit Size: Profit is a categorical variable that splits a measure of firms basic profit into quintiles. It serves as a control for firm profitability. The variable is constructed by subtracting the costs of labor, electricity, transportation, and raw materials from total sales. Costs, total sales, and thus profits per worker are converted from local currency units into US dollars using historical exchange-rate data. We expect profit to be positively associated with opportunity entrepreneurship. Profit Per Worker Size: Catppw is a categorical variable that splits a measure of firms basic profit per worker into quintiles. It serves as a control for firm productivity. The variable is constructed by dividing a base measure of profit by the total number of workers. The base measure of profit is in US dollars and thus profit per worker is also in US dollars. We expect catppw to be positively associated with opportunity entrepreneurship. Access to External Finance: Finance is a dummy variable that is a direct measure of whether a firm has access to external finance. A firm is considered to have access to external finance if it had access to funds from microfinance institutions, banks, or relatives and friends to finance the day-to-day operations of the business or purchase machinery, vehicles or other means of transport, equipment, land or buildings. This broader measure of access to external finance in the informal sector where most firms do not have access to bank loans. We expect finance to be positively associated with opportunity entrepreneurship. Cell Phone Usage: Cellphone is a dummy variable that indicates whether a firm uses cellphones it its operations. We expect cellphone to be positively associated with opportunity entrepreneurship. 22