IS THERE A WEALTH AFFECT? FINANCIAL AND HUMAN CAPITAL AS DETERMINANTS OF BUSINESS STARTUPS
Beth Crosa, Department of Sociology, The Ohio State University
Howard E. Aldrich, Department of Sociology, University of North Carolina
Lisa A. Keister, Department of Sociology, The Ohio State University
Access to resources is clearly an important factor in business start-ups, yet previous research has drawn conflicting conclusions about the relative importance of financial and human capital. We investigate this issue using the panel study of entrepreneurial dynamics (PSED), a unique new data source collected by the entrepreneurship research consortium (ERC). We use logistic regression to predict the likelihood of business formation as a function of various measures of wealth and human capital. We find that neither prior personal wealth nor income had any effect on business formation, but that education and other measures of human capital are very important to the business formation process. Women and certain minority groups, namely blacks and Hispanics, are much less likely to enter self-employment than non-minority men.
Entrepreneurship, the creation of new economic entities, is central to the evolution of organizations and economies (Aldrich 1999). Entrepreneurial activity is a vital component of national economic growth and development because it encourages innovation, fosters job creation, and improves global competitiveness for both firms and entire countries (Bednarzik 2000; Keister 2000). New business formation also shapes the nature of social and economic stratification in an economy (Haltiwanger & Krizan 1998). Organizations play an important role in distributing life chances and determining individuals’ social standing and chances for social mobility (Haveman and Cohen, 1994). Thus, the rate of new business formation and the nature of emerging economic entities provide the structure within which individuals acquire most of their economic resources and social recognition.
New business formation is also a key potential path for upward social mobility. Entrepreneurship and self-employment enable individuals to accumulate wealth, to expand their social contacts, and to improve their social and economic standing (Bates, 1997a; Fischer & Massey 2000; Keister 2000; Nee & Sanders 1985; Quadrini 1999). The families of entrepreneurs may also experience upward mobility, both immediately and over time. Many business owners, particularly those who create large firms, employ family members in their business ventures, and some pass on their businesses to their families, either during their lives or as part of an inheritance (Keister 2000). As a result, a small group of self-employed families accumulate great wealth and move to higher wealth classes over time (Quadrini, 1999). Entrepreneurs who successfully accumulate financial assets are also able to invest in their children’s human capital, and they may be able to expand their children’s social capital and occupational opportunities, as well (Nee & Sanders, 1996).
In this paper, we explore the factors that lead to nascent entrepreneurship in more detail than was previously possible. We draw on prior theoretical and empirical literature to develop a series of hypotheses relating financial and human capital to the likelihood of business formation. Following a theme in the literature, we test the proposition that net worth and income will both increase the likelihood of new business formation. We also propose that human capital will have an important effect on business start-ups. We use unique new data from the National Panel of Entrepreneurial Dynamics to explore these propositions empirically. These data allow us to examine our propositions on a sample of potential entrepreneurs that is unprecedented in its coverage of the population and that simultaneously allows us to contribute to resolving a number of ongoing controversies. Our findings offer strong support for some, but not all, of our hypotheses.
INFLUENCES ON NEW BUSINESS FORMATION
We focus on two sets of factors affecting startups: financial capital and human capital. We will examine other causes, but they will be treated as control variables in our analysis.
Financial Capital
Although entrepreneurship clearly has important social and economic consequences, we know little about the specific factors that lead to the creation of new businesses. Researchers agree that access to resources is an important influence on business start-ups, but previous studies have been able to draw only limited conclusions about the relative roles of financial and human capital. Researchers in this camp also argue that those with little personal wealth have higher failure rates in new business than their wealthier counterparts (Holtz-Eakin, Joulfaian, & Rosen, 1994).
Research on the impact of financial capital on new business formation has generated mixed results. Some researchers assert that financial capital is critical for entrepreneurship and that liquidity constraints inhibit start-ups (Bates, 1997b, 1997c; Dunn & Holtz-Eakin 1996; Evans & Jovanovic 1989; Fischer & Massey, 2000). They reason that business start-ups often require a substantial sum of money in order to buy the necessary equipment and supplies. This viewpoint emphasizes that equity, particularly from family wealth holdings, allows entrepreneurs to obtain credit, and those with little personal wealth simply cannot secure necessary start-up capital (Bates, 1990). Thus, those with high net-worth, high income, and home ownership are expected to be more likely than others to become self-employed (Fischer & Massey, 2000; Evans & Leighton, 1989; Bates, 1995). In support of this viewpoint, research has shown that obtaining money from an inheritance increases the likelihood of self-employment (Holtz-Eakin, Joulfaian, & Rosen, 1994).
Personal savings are often the key to funding new businesses. Financing through bank loans or investors can be difficult and disadvantageous for the small business owner for many reasons. For those with little or no wealth, financing through institutional loans can exact a high price in the long term. Because small businesses are higher risk clients for potential financiers, lenders often compensate by increasing the financial costs associated with the loans, making this a less appealing path to gaining business capital in comparison to personal savings. In addition to the high costs of using financiers, small businesses also incur the cost of identifying potential financiers and undergoing bonding activities to ensure firm legitimacy. Furthermore, Jurik’s (1998) study of home based businesses, which comprise a large proportion of all new businesses, found that few were eligible for bank loans.
Researchers who disagree with the emphasis on financial capital argue that too much importance has been placed on the availability of monetary assets. Many small businesses do not require large amounts of financial capital in their start-up phase. Data from a 1992 survey show that the majority of business owners started their firms with less than $5,000 (U. S. Bureau of the Census, 1992). Other research has also shown that personal wealth is not a major factor in new business ownership (Aldrich, Renzulli, & Langton, 1998). Home-based businesses, for instance, which accounted for half of all new businesses in 1992, often require little capital up front.
Furthermore, small business owners can often find ways around capital constraints. Many small business owners use financial “boot-strapping” methods to decrease capital needs in the start-up phase (Freear, Sohl, & Wetzel, 1995; Harrison & Mason, 1997). These methods include relatives working below market salary, using of the owner’s personal credit card for business expenses, borrowing from relatives, withholding the owner’s salary, taking on freelance assignments from other businesses, and leasing equipment rather than buying it (Winborg & Landstrom, 2000). New business owners may be forced to start out by relying exclusively on their own and their relative’s resources (Aldrich & Waldinger, 1990). Thus, we expect that access to financial capital will probably only slightly increase the likelihood of new business formation.
Hypothesis 1a: Household net worth will increase the likelihood of being a nascent entrepreneur;
Hypothesis 1b: Household income will increase the likelihood of being a nascent entrepreneur;
Hypothesis 1c: Home ownership will increase the likelihood of being a nascent entrepreneur.
Human Capital
In opposition to the view that wealth matters, however, are those who argue that there may be ways to compensate for a lack of personal wealth. These researchers contend that many entrepreneurs require little or no capital to begin forming a new business (Aldrich, 1999). Innovative potential entrepreneurs may find creative ways to finance new businesses, such as borrowing money from family or drawing on credit from credit cards. There is some evidence that human capital may also affect business formation. Human capital is defined as an investment in skills and knowledge that boosts earning power, and we believe it plays an important role in new business formation (Becker, 1964). Education, training, and workplace experience are the most common indicators of human capital used in labor force participation analyses, and these traits have been associated with the success of entrepreneurs (Bates, 1997; Evans & Leighton, 1989; Greene, 2000).
Education, for example, may help people develop skills and knowledge that are advantageous for organizing and operating a business. However, research on the relationship between education and new business start-ups has produced mixed results. Educational credentials may make it easier to find employment working for others, thereby reducing the attractiveness of self-employment. People can accumulate human capital relevant to business formation from their self-employed parents (Altonji & Dunn, 1991; Aldrich, Renzulli & Langton, 1998). Work experience, current self-employment, and age also increase entrepreneurship, although the effect of age probably declines near retirement (Waldinger, Aldrich & Ward, 1985).
Research on education’s impact on entrepreneurship has been inconclusive (Greene, 2000). Some researchers have found that better educated people are the most likely to become entrepreneurs (Bates, 1995; Bates & Servon, 2000; Fairlie & Meyer, 1996). In knowledge-based service industries, such as finance, real estate, and insurance, education is clearly relevant to business acumen. Education might also be more important for self-employed immigrants, in comparison to native small business owners, because skills gained through formal education allow immigrants to more effectively start and operate their own small businesses (Sanders & Nee, 1996; Portes & Zhou, 1992).
By contrast, education may be less important for native-born entrepreneurs because their higher education credentials give them ready access to employment in the formal job market, discouraging their pursuit of self-employment (Fischer & Massey, 2000). In 1992, over 55 percent of all business owners did not have a college degree (U.S. Bureau of the Census, 1992). Those who start small businesses in trades such as construction or carpentry have little need for a college degree; instead they draw on their acquired technical skills and on-the-job experience. Data from the General Social Survey in 1991 showed that education was negatively related to self-employment (Butler & Herring, 1991). A study of women entrepreneurs found that education had either no effect or a negative effect on entrepreneurship (Greene, 2000). Although education has not been very important for self-employment in the past, as the U.S. labor market becomes increasingly service and information based, education will probably play an increasingly important role in facilitating new business formation. Thus, we hypothesize that, on balance, education will be positively associated with an increased likelihood of entrepreneurship.
The children of the self-employed are more likely to become self-employed in their adult careers (Aldrich, Renzulli, & Langton, 1998; Altonji & Dunn, 1991). Some researchers have suggested that parents hand down their businesses to their children upon retirement or provide capital for new businesses (Robinson, 1984; Robinson & Garnier, 1985). Recent research does not support this claim (Aldrich, Renzulli, & Langton, 1998). Self-employed parents rarely keep their children in the family business, so inheritance does not explain why the children of self-employed parents are so often found in self-employment.
Intergenerational processes other than direct inheritance of the business thus appear to play an important role in the transmission of human capital including education and labor status (Altonji & Dunn, 1991, Keister & Moller, 2000). Self-employed parents may provide a specific type of human capital to their children, labeled “entrepreneurial capital.” Entrepreneurial capital is an extended version of human capital that includes experiences and skills inherent to self-employment. However, this form of capital is difficult to define operationally. Previous research which attempted to identify behavioral differences between those entrepreneurs whose parents were self-employed and those who were not, found no significant differences between the two groups in previous career paths or willingness to work long hours, nights, and weekends (Aldrich, Renzulli, & Langton, 1998).
Researchers have speculated that self-employed parents might encourage the development of entrepreneurial attitudes in their children through socialization. Parents may somehow be instilling a learned preference for self-employment in their children, possibly by providing work experiences at a young age, or through exposing their children to the entrepreneurial lifestyle and the social networks tied to their businesses (Aldrich, Renzulli, & Langton, 1998; Carroll & Mosakowski, 1987; Hout, 1989). We thus expect that having self-employed parents will positively affect entrepreneurship.
Age is another important proxy for human capital. Previous research has found that age is positively related to self-employment, until a certain age threshold is reached. Aging increases the likelihood of business ownership for several reasons. Age is strongly and positively correlated with work experience, fostering the development of entrepreneurial skills and attitudes (Aldrich, Renzulli, & Langton, 1998). Previous work experience in the field of self-employment increases a person’s perceived chances of success for a new business, thus encouraging those with the necessary experience to start their own businesses (Bruderl, Preisendorfer, & Zeigler, 1992). The years of working for others have allowed potential entrepreneurs time to build savings and make contacts that can help them with their new businesses. We thus expect that the likelihood of entrepreneurship will increase with age until a certain threshold age is reached (possibly near retirement age), after which the relationship becomes negative.
Research suggests that those who are currently self-employed are more likely to be involved in other new business ventures than those who are working for others. People already in business have many opportunities to develop valuable contacts, as well as accumulate the additional wealth that can use to begin a new venture. They are also well placed to spot new opportunities in their own or contiguous industries. Thus, we believe that persons already in business will be more likely than others to start another one.
Hypothesis
2: Human capital will have a positive affect on entrepreneurship, in
four specific ways:
a. Higher education will increase the likelihood of being a nascent entrepreneur
b. Age will have a curvilinear relationship to being a nascent entrepreneur
initially positive and then negative
c. Having self-employed parents will increase the likelihood of being
a nascent entrepreneur
d. Current self-employment will be positively associated with being a
nascent entrepreneur
We use data from the Panel Study of Entrepreneurial Dynamics to explore the relationship between financial and human capital and new business formation. The Entrepreneurial Research Consortium developed this project specifically to provide a basic description of the start-up process and to allow the testing of hypotheses regarding factors that effect different stages of the start-up process. The data consist of a panel of individuals in the early stages of business formation and a comparison group of non-entrepreneurs, and involves several collection phases. Individuals in the process of starting a new business are called “nascent entrepreneurs.” The first phase of data collection began in 1999 and the final stage of the project is not yet completed.
Individuals were identified through a random digit dialing telephone survey of 64,622 adults in the United States who were 18 years of age or older. Next, through a screening process, researchers identified nascents (those who were trying to start a new business alone or with others). Then, they located a randomly selected comparison group. The next research phase involved the completion of detailed phone interviews and self-completed questionnaires returned via mail. The response rate for the phone interviews was 74 percent. The phone interview (containing the data used in our analysis) had several different sections. It began with questions regarding general information about the new business and associated start-up activities. Respondents answered questions about start-up team members, the social networks in which they received support for self-employment, funding requirements, market and competition assessment, current labor force activities, work experience, migration, and family background characteristics such as birth order, parents employment history, household structure, income, and net worth.
Classification as a nascent entrepreneur required meeting several different criteria. First, individuals had to be trying to start a new business, alone or with others, and they had to be actively engaged in this during the past 12 months (6.1 percent of those screened). Second, they were expected to be owners in the new business. Third, eligible entrepreneurs were limited to those whose business efforts were in the start-up phase. Those who had already successfully started their new business did not qualify for the study. Likewise, individuals who had obtained an income from their new business that covered operating expenses and owners’ salaries for three months or longer were considered to have already started their new businesses and were excluded from the study.
Over 85 percent of those who qualified as nascents agreed to participate in the study. Two thousand people were approached about a national study of work and career choices and 70% of those agreed to participate, forming a comparison group. Supplemental funding from the National Science Foundation made possible an increase in the number of women participating so that they were slightly more than half of the nascent entrepreneurs in the final sample (Reynolds, 2000).
Variables
Table 1 presents the list of independent variables and definitions we use in the analysis. Further details on each individual variable are presented below.
Dependent Variable
We define entrepreneurship as a dichotomous individual level variable that is assigned a value of 1 if the respondent was in the process of starting a new firm, alone or with others, during the screening interview. A comparison group of respondents who were not attempting a new business were given a value of 0. We limit the analysis to those who were starting fully autonomous businesses and did not have backing from a corporation or venture capitalists.
Financial Variables
To measure net worth, respondents were asked the current net worth of their household. If they refused to answer this question, they were asked to indicate whether their net worth was above certain dollar amounts starting with $100,000 to $5,000,000. Next, we re-scaled our raw net worth values into units of 100,000 in order to scale up the coefficients and standard errors for the sake of easy interpretation of the coefficients, and the variable was logged The same steps were used in preparing household income for analysis. We created a dichotomous variable indicating whether the respondent owns their living residence.
Human Capital
Education, age, having self-employed parents, and current self-employment are all measures of human capital. For this study, respondents identified the highest level of education they had attained. From this question we created a dummy variable for each level of education identified, with the “college degree” omitted in our analysis as the reference category. We created a dummy variable indicating whether respondents had parents who were ever self-employed. Our age variable ranges from 18 to 93 years. We also include a squared term for age based on previous findings that the likelihood of entrepreneurship increases with age, peaking at 40 years old and then leveling out and declining by retirement (Bates, 1997). Finally, we created a dichotomous variable indicating whether respondents were currently self-employed.
Control Variables
Women and men have different self-employment rates. Women are slightly less likely to be self-employed than men, and recent studies indicate that women own approximately thirty-seven percent of all small businesses. However, current trends suggest that the number of women turning to self-employment is growing at a faster rate than for men. Based on these known differences, we control for gender in the analysis.
Minorities have different propensities toward new business formation. In the past, black business owners frequently lacked access to financial resources. Even today, access to credit is still not equal between blacks and whites, and loan amounts are smaller for black owners than for whites (Ando, 1988; Bates, 1997a). Blacks also tend to get lower returns on their educational investments than whites do. Black households have significantly lower levels of personal wealth than whites do, and this also hinders their ability to finance new business. Additionally, most minority groups, including Hispanics, have low levels of self-employment relative to whites (Bates, 1997a). We therefore control for ethnicity in our analysis.
We present our analysis in the form of three logistic regression equations in Table 2. We present them as nested models to permit an assessment of the relative power of our independent variables. Model 1 includes only the financial capital and control variables. Model 2 includes only the human capital variables and the control variables. Then, in Model 3, we include all variables.
Financial Capital
Some researchers have argued that access to financial resources is important to entrepreneurship, but previous studies have painted a mixed picture. Our first set of hypotheses, H1a, b, and c, predicted that financial capital would be positively associated with the likelihood of new business formation. In Model 1, income has a significant positive effect and net worth has a significant negative effect on being a nascent entrepreneur. However, when the human capital and control variables are entered into the analysis, in Model 3, neither financial variable remains statistically significant. Home ownership is also not significantly associated with being a nascent. Our results thus support the view that neither income nor wealth is sufficient to foster entrepreneurship.
Our findings suggest that the importance of financial assets may have been over-emphasized in the entrepreneurship literature. Entrepreneurs do not need a lot of money to start their business, and they can also find innovative ways to gain access to funds. A contingent view of financial capital seems appropriate, which is that access to financial assets is more important in some industries than others. For example, Bates (1997a) suggested that researchers should not generalize the importance of financial capital to entrepreneurs in all fields.
Human Capital
Hypothesis 2a predicted that higher educational qualifications would increase the likelihood of new business formation. As Models 2 and 3 show, those with a high school diploma or less have a significantly lower likelihood of being a nascent entrepreneur than those with higher educational qualifications. In the final model, they are only half as likely as those with higher educational credentials to become nascent entrepreneurs. None of the other coefficients for educational levels are statistically significant, suggesting that the crucial break is between having a high school degree versus any education beyond that level. Although there is some hint that people with a technical degree are less likely to become nascents, the coefficient is not statistically significant.
Age is an important proxy for years of work experience. Hypothesis 2b predicted that age would have a curvilinear relationship with being a nascent entrepreneur, initially positive and then turning negative. The coefficient estimates for age in Table 2 indicate a weak positive linear relationship between age and being a nascent. The squared term for age is negative, thus supporting our hypothesis of a curvilinear relationship.
In Table 2, we show that those with self-employed parents are more likely to be nascent entrepreneurs than those without self-employed parents. However, the relationship becomes insignificant after controls are introduced in Table 2. The apparent effect of self-employed parents is thus actually mediated through education and current self-employment status, as well as the parents’ ethnicity. Hypothesis 2c is thus not supported.
Hypothesis 2d predicted that those who are currently self-employed are more likely to become nascent entrepreneurs. The coefficient estimates in Table 2 provide strong support for our hypothesis. In Model 2, current self-employment has a strong effect on being a nascent entrepreneur, and the coefficient is not diminished when the financial assets variables are added in Model 3. When the coefficient of 1.50 is converted to an odds ratio, we see that currently self-employed persons are about 4.5 times more likely to be nascent entrepreneurs than those working for others.
We included controls for race and sex in Table 2. Our results show that women are only about half as likely as men to be nascent entrepreneurs, taking into account their level of financial assets, education, age, and ethnicity. Across all models, Blacks and Hispanics are significantly less likely than whites to be involved in new business formation. In the final model, Blacks are only about 60 percent as likely to be nascent entrepreneurs as whites, and the likelihood for Hispanics is only about 38 percent of that for whites.
The relative importance of access to financial versus human capital resources has received little attention in studies of nascent entrepreneurship. In this paper, we took advantage of longitudinal data focused on nascent entrepreneurs (those who were trying to start a new business) to examine the importance of these factors in predicting the likelihood of new business formation. Regression estimates indicated that wealth is not a major predictor of new business formation, once controls are introduced for human capital and basic demographic factors. By contrast, workers’ education and age levels do affect their likelihood of becoming a nascent entrepreneur. Our results suggest that those with a high school diploma or less are at a disadvantage.
Although fields such as construction that rely more heavily on technical skills may be less sensitive to education credentials, overall, it seems that education improves the chances that an individual will attempt a new business idea. Besides providing communication and technical skills, higher education credentials may improve someone’s ability to find innovative ways around financial constraint. Those with limited educational backgrounds may not be acquiring the necessary skills, work experience, contacts, or financial assistance for business formation.
In summary, financial assets are not a major predictor of entrepreneurship, whereas age, education, current self-employment status, race, and gender are important to becoming a nascent entrepreneur. The black population, which historically has struggled with poverty, segregation, an inadequate education system, as well as reduced access to credit, is among the least likely to become entrepreneurs. We also found this pattern for Hispanic respondents. Overall, the most disadvantaged members of society are the least likely to attempt self-employment.
We noted that some theorists have argued that entrepreneurship can be a route to upward social mobility. However, our results show that the mobility effects of entrepreneurship may be limited to only certain segments of the population. Those with few resources who do enter self-employment are less likely to persist in the endeavor, and generally receive fewer returns for their efforts (Bates, 1997).
Future research should address the impact of external market forces, such as level of competition in the market, and entrepreneurial environments, such as geographic proximity and government policy, in conjunction with financial, and human capital resources.
Caveats
Net worth may be less significant in the early stages of business start-ups, but have increasing importance later on for business survival. Researchers suggest that net worth plays a more important role in later stages of business development. Entrepreneurs who cannot borrow capital might be at a disadvantage in comparison to those with personal financial resources. In support of this viewpoint, researchers have found that those with more capital resources are more likely to survive in self-employment and their firms are more successful (Holtz-Eakin, Joulfaian, & Rosen, 1994).
It is also important to note the time period when this data was collected. The late 90’s was a period of great economic prosperity and it marked the beginning of popular Internet start-up companies, dot-com companies, and a soaring stock market. Computer based companies, and an increase in the number of free-lance workers may have facilitated working from home and decreased start-up costs, because these businesses are often customer service based.
CONTACT: Howard E. Aldrich, Sociology Department, CB# 3210, UNC-CH, Chapel Hill, NC 27599-3210; (T) 919-962-5044; (F) 919-962-7568; howard_Aldrich@unc.edu
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