The Organizational Advantage? Social Capital, Gender, and Small Business Owners. Access to Resources

Transcription

1 The Organizational Advantage? Social Capital, Gender, and Small Business Owners Access to Resources By Amy B. Davis and Howard E. Aldrich Department of Sociology, CB #3210, University of North Carolina, Chapel Hill, NC Phone: Phone: Paper prepared for presentation at the 2000 American Sociological Association meetings. Draft: Comments welcome 1

2 Abstract Under the competitive conditions of modern economies, the resources directly controlled by owners frequently are not sufficient to ensure their small businesses survival and growth. Instead, owners often need to supplement what they personally control by using their social capital resources embedded in their social relations with others -- to obtain financing, expert advice, and other resources. In particular, business owners may be able to create and enhance their social capital by joining and participating in voluntary associations, thus making connections beyond their immediate circle of relationships. Voluntary associations present opportunities for members to make connections with persons unlikely to be encountered otherwise. Therefore, membership in organizations may increase the heterogeneity of owners personal networks. Increased heterogeneity, in turn, may lead to improved access to resources, and as a result, enhance the odds of success and survival for owners businesses. However, benefits from social capital created through organizational memberships may flow unevenly to men and women. Using data from the Entrepreneurial Development Study, we examine the contribution that voluntary association membership and participation make to small business owners access to critical organizational resources. Our results indicate that memberships in instrumental associations and intensive organizational involvement increase business owners odds of gaining access to some resources, such as expert advice. But, the overall impact of membership is limited. We also show that women do not suffer any notable disadvantages in this process, compared to men. Indeed, memberships in several types of instrumental associations actually appear to benefit women more than men. 2

3 Under the competitive conditions of modern economies, the resources directly controlled by owners frequently are not sufficient to ensure their small businesses survival and growth (Aldrich, 1999: ). Instead, owners often need to supplement what they personally control by using their social capital resources embedded in their social relations with others -- to obtain financing, expert advice, and other resources. In particular, business owners may be able to create and enhance their social capital by joining and participating in voluntary associations, thus making connections beyond their immediate circle of relationships. As Portes and Zhou (1996:228) concluded, successful entrepreneurs are not isolated individuals. In this paper, we examine the contribution that voluntary association membership and participation make to small business owners access to critical organizational resources. Benefits from social capital created through organizational memberships may flow unevenly to men and women. Memberships may bring advantages to women participants, but the advantages may be circumscribed because of various societal constraints. Voluntary associations might provide opportunities for women to make contacts that may otherwise be unlikely to freely form in the labor market, thus helping them overcome barriers to resource mobilization. Alternatively, voluntary organizations may simply mirror the pervasive sex segregation present in other sectors of society, and thus provide no net benefits to women. In this paper, we explore the likelihood of both scenarios. Plan of the paper In the first section of this paper, we discuss the relevant social capital literature and its implications for small business owners' abilities to obtain resources. We derive hypotheses about the resource-generating benefits of organizational memberships, and the extent to which 3

4 they might be gender biased. Next, we discuss the data set, the Entrepreneurial Development Study (EDS), and measures used in our analysis. We then discuss results derived from logistic regression analyses. Finally, we address some limits to our research and discuss further opportunities for research. Social Capital Sociologists often contrast "human" with "social" capital. Human capital inheres in the education and experience that people acquire over their life course. Social capital refers to the ability of actors to secure benefits by virtue of membership in social networks or in other social structures (Portes, 1998: 6). Lin characterized social capital as an investment in social relations by individuals through which [persons] gain access to embedded resources to enhance expected returns of instrumental or expressive actions (Lin 1999:39). Social capital gives persons access to valued assets, such as information, referrals, resources, and support (Ibarra 1997:91). These assets are limited, and therefore persons must compete for them. The literature on social capital implies that social capital can benefit societies, communities, and individuals (Kennedy et al., 1998; Putnam, 1995; Wilson and Musick, 1997b). With regard to individuals, researchers have found that social capital positively affects labor force participation, income, and job mobility (Campbell et al. 1986, Lin et al. 1981a, Lin et al 1981b, Marsden and Hurlbert 1988, Stoloff et al. 1999). For example, Fernandez and Weinberg (1997), in their study of a retail bank, found that job applicants referred by current employees more often received interviews and subsequent job offers than non-referred applicants. An important assertion of this literature is that social capital affects an individual s economic well being net of human capital factors, such as education and experience (Burt 1998). 4

5 Memberships in voluntary associations are important to the generation of social capital because they bring individuals together and are integrative forces in society. Popielarz and McPherson (1995:698) defined voluntary associations as formal and informal groups not directly part of family, government, religion, or economic firms. Knoke (1986:2) listed an expansive range of voluntary associations, which included labor unions, churches and sects, social movement organizations, political parties, professional societies, business and trade associations, fraternal and sororal organizations, recreational clubs, civic service associations, philanthropies, social welfare councils, communes, cooperatives, and neighborhood organizations. Voluntary associations can provide forums for political action, outlets for hobbies and special interests, and opportunities for serving a community. Recent conservative estimates put the number of voluntary associations in the United States at well over one million (Aldrich, 1999: 352). Voluntary organizations are also arenas for the generation and maintenance of social networks (McPherson and Smith-Lovin, 1986:62). Organizations, including business firms, may be particularly adept at generating social capital if they create a culture conducive to creating and sharing knowledge (Nahapiet and Ghoshal 1998:259). Popielarz (1999a: 244) noted that memberships in voluntary associations shape personal networks because voluntary groups offer exposure to new social contacts. For example, membership in voluntary associations is positively associated with network size, which is associated with more contacts with diverse others (Popielarz 1999b). Stoloff et al. (1999) found that membership in voluntary associations significantly increased the odds of labor force participation for women. These studies thus imply that the resources embedded in people s networks of social relations, many of which are formed in voluntary associations, determine the amount and quality of social capital. 5

6 Social Capital and Small Business Owners Heterogeneity clearly exists in the financial performance of businesses (United States Census, 1992). Although some of this heterogeneity may be attributed to differences in human capital, as well as business size and industry, some is also due to owners having differential access to external resources. Some owners may also have more external resources -- such as advice from experts or access to loans -- which enhance the viability of their businesses, whereas others do not. Just as networks have been shown to provide advantages to jobseekers, so too could social capital, in the form of connections made via voluntary associations, benefit owners in their quest for scarce resources. We are particularly interested in six types of resources that owners might learn about through connections made via voluntary association membership. These resources are: financial or accounting assistance, legal assistance, assistance in obtaining business loans or making contacts with investors, finding a supplier or vendor in their line of business, assistance in locating business property, and meeting someone who is an expert in their line of business. Many business owners need assistance in managing their taxes, records, and capital flow, understanding financial and accounting regulations, and obtaining financial capital to start or grow their business. According to the Characteristics of Business Owners study, only about 10 percent of individual business owners borrow capital through commercial bank loans (U.S. Census, 1992). The majority of these businesses were started with less than $5,000. When business owners wish to obtain loans, many find that commercial loans from banks are too difficult and costly to secure. Banks view funding new businesses as risky, and therefore require extensive background checks, high interest rates, and a large amount of collateral for new business loans (Aldrich 1999: ). Uzzi (1999), in a national study of small businesses, 6

7 found that social capital seemed to improve owners access to business loans and also lower the cost of loans. Owners with contacts in banks more often received loans and received them at a lower rate than owners without contacts. Owners may also need legal advice, given the potential for opportunistic behavior by customers, vendors, and others (Williamson, 1981). Information involving business property often circulates informally in the business community, and thus ties to people with access to such information are often critical in obtaining property before it goes on the open market (Cater, 1981). Establishing working relations with new suppliers or vendors may be facilitated if they are met through after-hours connections, especially given the reluctance of owners to change such relations, once established. Perhaps most important, many owners can benefit from advice provided by an expert in their same line of work. Expert advice can be a substitute for gaining direct experience, as well as a shortcut to acquiring tacit knowledge (Aldrich, 1999: 93-94). Business owners receiving advice from an expert may thus learn the effective routines and competencies shared by other businesses in their industry (Aldrich, 1999: 89-91). Our review of the situation of small business owners suggests that, in addition to relying on their own knowledge and experience, they may benefit from opportunities to make contact with and learn from others in their community. If organizations create opportunities for owners to meet experts, loan officers, or other relevant others, then organizational memberships serve as a form of social capital and might enhance the odds of business survival and success. See Figure 1 for a graphical illustration of our argument. 7

8 Dimensions of Organizational Memberships Voluntary organizations are diverse in form as well as purpose. The organizational dimensions that we hypothesize will affect the social capital generated through organizational membership include the type, size, extent members are involved, and the gender composition of the organization. Organization Type. The purpose of an organization may influence the type and amount of social capital it produces for its members, according to several studies. Stolle and Rochon (1998) found that membership in political, economic, and community groups enhanced political and community participation, a particular type of social capital. By contrast, they found that membership in cultural, personal interest, and economic groups enhanced trust and efficacy, another type of social capital. Participation resulted in members becoming more involved and integrated in their community. Rotolo (1999) showed that participation in voluntary associations, depending on type, led to an increase in different forms of social capital. He found that participation in school-service groups might increase levels of social trust... whereas participation in hobby groups might encourage socialization between group members outside official group activities Rotolo, 1999:210). Neither the Stolle and Rochon nor the Rotolo studies directly demonstrated that organization members received instrumental or economic benefits from their involvement. However, we can infer from their research and others (Knoke, 1986) that the type of social capital produced by organizational membership depends on the purpose of the organization. We propose that voluntary associations explicitly organized along occupational, professional, and business lines are the most likely to produce instrumental benefits. By contrast, associations organized along social, civic, and religious lines are more likely to return expressive benefits to 8

9 their members (McPherson and Smith-Lovin, 1982). Whereas membership in both types of organizations allows people to develop useful connections with others, we propose that instrumentally oriented associations are the most valuable. Hypothesis 1: The greater an owner s number of memberships in instrumentally oriented voluntary associations, such as business and professional organizations and Chambers of Commerce, the greater their access to resources. Organization Size. Most people form only a limited number of strong ties, given the investment of time and effort required to keep them active (Fischer, 1982; Marsden and Campbell, 1984). By contrast, people can form a large number of weak ties that need only be activated occasionally to keep them of potential use. When self-help books on aggressive careerbuilding strategies mention networking, they are referring to the weak ties that can be created through activities such attending meetings, doing volunteer work, and finding other ways to interact with a large number of potentially useful people. The more members in an organization, the more potential contacts a member can form within that organization. Hypothesis 2: Memberships in larger organizations provide more access to resources than memberships in smaller organizations. Involvement. Organizational involvement has consequences for the diversity of an organization, as well as the social capital an organization produces for its members. McPherson and Rotolo (1996:186) noted that individuals have finite time and other resources for participating in groups, and thus organizations must compete for members limited time and resources. McPherson and Rotolo distinguished between active memberships, in which members are involved in organizational activities for at least one hour a month, and nominal memberships, in which individuals spend less than one hour a month on organizational activities. 9

10 McPherson and Rotolo (1996) found that competitive pressures on organizations are much stronger on active memberships than on nominal memberships. As a result, organizations characterized by nominal memberships are more diverse than those memberships characterized by active memberships. Thus, the relationship between involvement and the social capital an organization produces for its members is complex. Whereas increased involvement would seem to improve social capital, it also seems to reduce organizational heterogeneity as well as shorten the length of membership. For example, Cress et al. (1997) found that highly involved members had shorter lengths of membership than less involved members. They attributed this finding to a possible burnout effect in which members become more dissatisfied with their organizations as their involvement increases. However, if they are instrumentally oriented, they the most active members may simply leave because they have reaped all the benefits possible in the short time they were active. Therefore, a tension exists between voluntary association involvement, organizational diversity, and duration of membership, possibly weakening the relationship between involvement and social capital. We thus propose competing hypotheses: Hypothesis 3a: Owners who participate more intensively in their organizational memberships will gain more access to resources than those who participate less intensively. Hypothesis 3b: Owners who participate intensively in their organizational memberships will gain less access to resources than those who participate less intensively. Gender Composition of Membership. We are specifically interested in two possible effects of the sex composition of voluntary associations on social capital: the effect of sex composition itself, and a possible differential effect for women versus men. Just as many studies have found that occupations and work organizations are sex segregated, so also has research 10

11 found that voluntary associations are sex segregated. Given that voluntary associations play a role in shaping people s personal networks, gender differences in voluntary association composition may result in gender differences in resource acquisition. McPherson and Smith-Lovin (1986) studied organizational sex segregation using a sample of 10 Nebraska cities. They found that over half of the organizations in their sample had exclusively women members; another 20 percent of the organizations had only men members. In addition to finding that voluntary associations were sex segregated, they also found that women s memberships generated fewer and less diverse networks than did men s memberships. They found that the average man s organizational membership generated face-to-face contact with 37 total persons, eight of whom were women. By contrast, they found that the average woman's organizational membership generated face-to-face contact with only 29 total persons, four of whom were men. McPherson and Smith-Lovin (1986) argued that voluntary organizations are sex segregated and leave women disadvantaged in obtaining information in men-dominated fields 1. They criticized the integration hypothesis that contends that organizations are opportunities for people from different locations of society to interact. Instead, they conjectured that organizations are sorting mechanisms that segregate people according to ascribed and achieved characteristics (651). They charged that organizations operate directly in some instances to maintain male privilege as a structural feature in our society (77). Hypothesis 4: Memberships in men-dominated organizations generate more access to resources than memberships in non men-dominated organizations. 1 We use men-dominated as short-hand to refer to occupations or organizations which have at least 75% men members or occupants. 11

12 If men-dominated organizations are indeed where the most influential business people congregate, then women have an added incentive for membership in them. Confirming McPherson and Smith-Lovin s (1986) findings, Popielarz (1999a) found that men belong to more sex-integrated organizations than do women. Popielarz (1999a: 248) argued that, in addition to women s memberships being sex segregated, membership in women s groups offer very limited contact with persons of different ages, education, and marital and work statuses. As a result, the ties women form within voluntary associations are significantly less likely to be information-rich bridging weak ties (247). However, Ibarra s research (1997) suggests that we should be cautious in speculating about the consequences of membership in women-dominated organizations. She found that successful women differed from unsuccessful women and both successful and unsuccessful men because they sought out other successful women for their instrumental networks. Ibarra conjectured that women benefit from instrumental networks with other women in two ways. First, instrumental networks with other women provide women with information about, and assistance with, gender-specific business issues. Second, because of women s underrepresentation in management, deliberately seeking other women for instrumental networks means women must make connections beyond their immediate network; as a result, they can increase their network heterogeneity. These studies suggest that women-dominated organizations might fail to generate advantageous social ties. If McPherson, Smith-Lovin, and Popielarz are correct, the sex composition of voluntary associations critically affects the development of women owners social capital because many of the areas in which they need assistance are high status, mendominated areas like law, management, and finance. If women are shut out of men-dominated 12

13 voluntary associations, they cannot make the connections needed to prosper in business. By contrast, if Ibarra is correct, then ambitious women will find ways around their exclusion, possibly by forming connections with other women, regardless of the sex composition of the organizations to which they belong. Hypothesis 5: Women gain more access to resources in men-dominated organizations than do men. Gendered Benefits by Type of Organization Campbell (1988) found that women s networks were less heterogeneous in occupational composition than men s networks. Family responsibilities and characteristics as well as geographic mobility related to a spouse s job explained much of this gender difference. She argued that, as a result of their restricted networks, women may be less likely to search for and find jobs through their networks or gain unsolicited information about job openings (Campbell 1988:196). Although Ridgeway and Smith-Lovin (1999:194) found that men and women have the networks of the same size, they also concluded that women typically have a higher proportion of contacts with kin and neighbors than men. Renzulli, Aldrich, and Moody (1999) and Marsden (1987) found similar gender differences. Moore (1990) found that gender differences in network heterogeneity were largely associated with structural factors. Restricted network heterogeneity means that women s networks will contain more redundant information. Moore found that full-time employment increased network heterogeneity and decreased the extent to which women s networks included kin. These studies suggest that women owners, to the extent that they labor under some of the same disadvantages as other employed women, would benefit substantially from opportunities to 13

14 increase the heterogeneity of their social ties. Voluntary associations provide an opportunity for them to do so. In particular, business and professional associations provide an arena in which women can make non-kin contacts. Hypothesis 6: Women gain more from membership in instrumentally oriented voluntary associations than men in terms of increased access to resources. Other Factors Affecting Organizational Social Capital Only those who join and participate in voluntary associations generate social capital from them. However, people do not participate equally in organizations. Instead, demographic characteristics and occupational and educational characteristics affect the extent to which individuals participate. These characteristics have implications for the distribution of social capital among individuals. Knoke (1986) found that men tended to participate more than women do and that whites tended to participate more than nonwhites. By contrast, Popielarz (1999b) found that men and women had similar affiliation rates. She also found that being white, older, religious, and highly educated, as well has having large networks, increased one s rate of voluntary association affiliation. We control for socio-demographic characteristics in our analyses. Summary of Our Expectations Membership in voluntary associations should provide benefits for business owners. In particular, owners should benefit from connections they form with diverse others within organizations that enhance their access to important resources. As organizational size and a member s involvement increases, opportunities for access to resources should also increase. Memberships in more instrumentally oriented organizations should provide more benefits to business owners than others. Gender may affect the social capital returns to organizational 14

15 membership resulting from organizational sex segregation, with women gaining more benefits from men-dominated organizations and instrumentally oriented organizations than men. Sample We use data from the Research Triangle Entrepreneurial Development Study to explore the social capital of business owners. Participants were located in the Research Triangle area of North Carolina, an area previously studied by Campbell (1988), Kalleberg et al. (1990), Aldrich et al. (1989), and others. Campbell analyzed gender differences in job-related networks by contacting people through the firms that employed them in the region. Kalleberg et al. (1990) and Aldrich et al. (1989) used the same region to study differences in the coverage of various organizational sampling frames. Luger and Goldstein (1991) found that economic development efforts in the Research Triangle area were similar to programs in other regions that attempted to promote growth through science parks. Based on these studies and others, we believe the Research Triangle area of North Carolina is a valuable laboratory in which to study business startup and growth. The respondents in this sample were selected from people with entrepreneurial or business activity memberships and involvement, drawn from organizations in Durham and Wake Counties, North Carolina, participants in technical college small business classes in Wake County, and a random sample of new business owners in Wake County. The sample thus included information on current small business owners. The randomly chosen subsampleallowed Reese (1993) to show that sample source was not a statistically significant predictor of network composition or networking activities. 2 2 In analyses not shown here, we replicated and extended Reese's (1993) test for possible selection bias in our sample. We found that sample source was not a significant predictor of our dependent variable, as the groups did not significantly differ from one another. 15

16 Two waves of information were collected, the first between 1990 and 1991, and the second in 1992 (see Reese 1993, Reese & Aldrich 1995 for a full description of the data.). Phase one was a short mailed questionnaire, and phase two involved an in-depth telephone interview with those who returned the questionnaire. In phase one, 659 questionnaires were mailed out and 444 returned. Telephone interviews were completed for 353 of the respondents who returned a mailed questionnaire. The survey thus had a completion rate of 67 percent of those who received a mailed questionnaire and a response rate of 54 percent of the original sample that completed a mailed and telephone questionnaire. These rates are comparable to those in other studies of entrepreneurs (Birley et al. 1990, Cooper & Dunkelberg 1987, Kalleberg and Leicht, 1991). We used only those wave one respondents who completed a phone interview, as they were asked the questions we used to construct our independent variables. In this paper, we analyze data from the 287 business owners, 210 of whom were men and 77 of whom were women. Measures Dependent variables measure respondents access to legal, financial, loan, property location, and expert assistance, as well as connections to a vendor or supplier in their line of business. Table 1 presents descriptions and frequencies of these variables. Independent variables measure varied dimensions of organizational memberships. Table 2 presents descriptions, means, and standard deviations for each of the independent variables. In addition to measuring the number of organizational memberships (ranging from 0 to 5), we also measured the type, potential connections, involvement, and sex composition of organizational memberships. Organizational type is measured by a classification scheme that includes six categories, three representing instrumental memberships and three representing expressive 16

17 memberships (McPherson and Smith-Lovin, 1982). Business memberships include a diverse array of organizational memberships, from memberships in networking organizations, to membership in the Better Business Bureau, to a position on another business board of directors. Chambers of Commerce memberships could include memberships in the Raleigh, Durham, Chapel Hill, Cary, and or other Chambers of Commerce. The final instrumental organizational membership measure is the number of memberships in professional associations. Expressive memberships include memberships in social, civic, or religious organizations. Expressive memberships were treated as the implicit baseline category of membership in our regression analyses and were excluded from the regressions. Potential connections are computed by summing across the membership size of all the organizations to which a respondent belonged. Involvement is measured by the total number of organizational meetings a respondent attends yearly. The number of men-dominated organizations is calculated by summing the number of organizations to which a respondent belongs in which all or most of the members are men. The number of women-dominated organizations is calculated by summing the number of organizations to which a respondent belongs in which all or most of the members are women. The number of sex-integrated organizations is calculated by summing the number of organizations to which a respondent belongs in which men and women members are equally represented. Only men-dominated organizational memberships will be included in our analyses, with non-men dominated organizations the implicit contrast category. Control Variables include human capital, business, family, and demographic measures that provide alternative explanations of organizational affiliation and resource acquisition. Human capital variables include whether a respondent has at least a bachelor s degree, whether a 17

18 respondent is running his/her first business, and a respondent s full-time employment experience. Business variables measure the age of the business in terms of how many years respondents have owned their businesses and the size of the business in terms of employees 3. Family indicators include whether a respondent is married and the number of children in his/her family. Demographic characteristics include race, age, and gender. Table 3 presents descriptions, means and standard deviations for each of the control variables. Results In the following section, we will first discuss descriptive findings of our measures. We then discuss the results of our logistic regression analyses. Descriptive Findings As the results in table 1 show, most respondents have access to the six business-related resources. In order of increasing scarcity, the resources are financial and legal advice, obtaining loans or investors, connections to a supplier or vendor, property location assistance, and expert advice. Table 1 also shows that although women are slightly less likely than men to have access to these resources (excluding financial assistance), the differences are not significant. Because almost all respondents had access to legal and financial advice, we did not include them as dependent variables in our logistic regression analyses. 3 Size of business is figured by the summation of the number of full-time employees and half of the number of parttime employees. 18

19 Table 1: Dependent Variables by Gender * Variable Name Definition Percent Yes Do you know someone you could go to: N Total Men Women Financial Contact for financial or accounting assistance (taxes, records, capital flow)? Legal Contact for legal assistance in business matters? Loan Contact for assistance in obtaining business loans or investors? Supplier/Vendor who is a supplier or vendor Contact in your line of business? Location Contact to for assistance in locating business property? Expert Contact who has several years of experience in your line of business? * No significant differences in access to resources were found between men and women. Table 2 shows that men and women do not significantly differ from one another in the total number of organizational memberships, potential connections, and involvement. However gender differences do exist in terms of the type and sex composition of their memberships. Men are significantly more likely to belong to professional associations than are women. Men-dominated organizations are the most popular voluntary association memberships for both men and women. Men belong to more men-dominated organizations than do women, on average, and women to more women-dominated organizations than do men. Women also much more likely to belong to men-dominated organizations than men are to belong to womendominated organizations. Indeed, out of a group of ten men, we would expect only one to belong to a women-dominated organization. By contrast, the average woman owner in our sample belongs to about one women- and one men-dominated organization. 19

20 These results contradict somewhat Smith-Lovin and McPherson s (1986) and Popielarz s (1999a) findings that women s organizational affiliations were more homogeneous than men s organizational affiliations. Our results may differ from theirs because our sample consists entirely of business owners, who are probably more instrumentally oriented than a random sample of the population. 20

21 Table 2: Independent Variables by Gender n=272 Mean Variable Name Description Total Men Women Total memberships^ Total number of organizational memberships (1.84) (1.80) (1.93) Instrumental Memberships Business memberships Number of memberships in business organizations (.74) (.68) (.88) Professional memberships Number of memberships in * professional organizations (.99) (1.04) (.82) Chamber memberships Number of memberships in Chambers of Commerce (.57) (.59) (.52) Expressive Memberships Civic memberships^ Number of memberships in civic organizations (.64) (.63) (.66) Social memberships^ Number of memberships in social organizations (.44) (.44) (.45) Religious memberships^ Number of memberships in religious organizations (.44) (.46) (.39) Potential connections Size of all memberships (232.08) (231.04) (232.67) Involvement Number of organizational meetings attended annually (43.43) (45.39) (36.82) Men-dominated memberships Number of memberships in men *** dominated organizations (1.41) (1.41) (1.15) Women-dominated memberships^ Number of memberships in women *** dominated organizations (.63) (.32) (.92) Representative memberships^ Number of memberships in sex representative organizations (.86) (.90) (.72) *=<.05 **=p<.01 ***p<.001 two-tailed tests Standard deviations in ( ). ^=variable does not appear in regression analyses 21

22 Men in this sample have more human capital on all of our measures. They are significantly more likely to have a bachelor s degree, are significantly less likely to be running their first business, and have significantly more years of employment experience. Men also have larger businesses than do women, confirming findings from previous studies (Aldrich and Langton, 1998). They have significantly more employees than women, but men and women do not significantly differ in the age of their businesses. Men are significantly more likely to be married and have significantly more children than women in this sample. No significant gender differences exist by race or age. To summarize, we find that men have more human capital and larger businesses than do women, and also more familial obligations. 22

23 Table 3: Control Variables by Gender n=272 Mean Variable Name Description Total Men Women Bachelor Does Respondent have a *** bachelor's degree? (1=yes) (.41) (.35) (.48) Full-time Years of full-time * employment experience (3.80) (3.81) (3.61) First business Is this Respondent's first business? ** (1=yes) (.46) (.48) (.38) Business size raw^ Number of employees in business * (32.34) (37.08) (10.58) Business size logged Number of employees in business plus * logged base 10 (1.27) (1.34) (1.01) Business age Years of ownership of the business (5.04) (5.06) (5.04) Married Is Respondent married? (1=yes) ** (.40) (.36) (.47) Children Number of children of Respondent ** (1.16) (1.23) (.89) Age Age of Respondent, in years (9.31) (9.24) (9.52) Minority Respondent's race (1=minority) (.21) (.22) (.20) Woman Respondent's gender (1=woman) (.45) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests Standard deviations in ( ). ^=variable does not appear in regression analyses 23

24 Table 4: Frequencies and Percentages of Industry of Business by Gender Agriculture Mining Construction Manufacturing Transportation, Communication, Electricity, Gas, and Sanitation Wholesale Trade Retail Trade Real Estate, Finance, and Insurance Services Total Total Men Women % % % % % % % % % % % 1.51% % % % % % % % % % % % % % % % % % % Chi-square=9.78 (p=.281) Logistic Regression Analyses were conducted for four dependent variables: loan assistance, location assistance, expert advice, and supplier or vendor advice. We ran a baseline model, with just human capital, business characteristic, and socio-demographic variables, and then a full model than included organizational membership variables. We used a Chi-square test to assess the significance of the full model, compared to the baseline model. In three of the four cases, the difference was significant at the.05 level, and in the remaining case, it was significant at the.10 level. 24

25 Tables 5a through 5d show the odds-ratios and standard errors for explanatory and control variables. The results for each of the dependent variables suggest that organizational affiliations increase business owners access to these resources. They also show that racial differences exist in access to resources, but that gender has no direct affect on access to resources. 25

26 Table 5a: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=Loan Contact n=272 Null Baseline Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business ** Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant *** psuedo-r Log Likelihood * (df=12) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05 ++=p< =p<.001 one-tailed tests 26

27 Table 5a: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=Loan Contact n=272, continued Organizational Interaction Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business ** ** Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children * * Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement * Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant psuedo-r Log Likelihood * (df=6) (df=6) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05, ++=p<.01, +++=p<.001 one-tailed tests 27

28 Table 5b: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable= supplier or vendor contacts n=272 Null Baseline Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children * Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant *** psuedo-r Log Likelihood (df=12) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +p=<.05 ++=p< =p<.001 one-tailed tests 28

29 Table 5b: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable= supplier or vendor contacts n=272, continued Organizational Interaction Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children * * Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement * * Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant Psuedo-R Log Likelihood (df=6) (df=6) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +p=<.05 ++=p< =p<.001 one-tailed tests 29

30 Table 5c: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=expert contact n=272 Null Baseline Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business Age of Business Family Married * Children Demographic Age Minority * Woman Gender * Family Woman*married * Woman*children Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant *** puedo-r Log Likelihood * (df=12) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05 ++=p< =p<.001 one-tailed tests 30

31 Table 5c: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=expert contact n=272, continued Organizational Interaction Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree * Years of Full-time experience First business Business Variables Size of Business Age of Business Family Married * * Children Demographic Age Minority * * Woman * ** Gender * Family Woman*married * ** Woman*children Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement *** ** Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant psuedo-r Log Likelihood * (df=6) * (df=6) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05 ++=p< =p<.001 one-tailed tests 31

32 Table 5d: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=location contact n=272 Null Baseline Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business *** Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant *** psuedo-r Log Likelihood * (df=12) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05 ++=p< =p<.001 one-tailed tests 32

33 Table 5d: Logistic Regression Analysis Coefficients are presented as odds-ratios Dependent Variable=location contact n=272, continued Organizational Interaction Variable Coef Std Error Coef Std Error Control Human Capital Bachelor's Degree Years of Full-time experience First business Business Variables Size of Business ** ** Age of Business Family Married Children Demographic Age Minority Woman Gender * Family Woman*married Woman*children Organizational Business Organization Professional Association Chamber of Commerce Potential Connections Involvement Men-dominated Organization Interaction terms Woman*business organization Woman*professional association Woman*chamber of commerce Woman*potential connections Woman*involvement Woman*men-dominated organization Constant psuedo-r Log Likelihood * (df=6) (df=6) *=p<.05 **=p<.01 ***=p<.001 two-tailed tests +=p<.05 ++=p< =p<.001 one-tailed tests 33