3.2.1 Cableado Estructurado

In this chapter, I discuss the data and methods used to test my hypotheses regarding status, teams, and entrepreneurial outcomes. I first discuss the data collection methods used in the PSED and the rationale behind them. I then discuss the

operationalization of my concepts and the methods used to create variables in the analysis. I then include a brief discussion of descriptive statistics of the variables of interest by gender and by the closeness of relationships among team members for those respondents on teams. I then review the regression methods used and finally discuss diagnostics for particular statistical problems and their remedies.

Data

To answer my research questions, I used data from the Panel Study of

Entrepreneurial Dynamics (PSED-I). The principal investigators for the PSED-I aimed to create a nationally representative sample of nascent entrepreneurs and a comparison group. They used random digit dialing and a screening questionnaire to determine if individuals were actively involved in starting businesses. Those with established businesses were excluded from the sample, defined as individuals with three or more months of positive cashflow generated through their ventures (see Appendix A, page 461 in Gartner et al. 2004). A comparison group included individuals who were not actively starting a business. Because of the interest in the influence of race and gender on

entrepreneurial experiences and processes, women and minorities were oversampled to ensure adequate representation. If not for this oversampling, the random digit dialing would have not generated sufficient numbers of women and minorities because women are underrepresented in the entrepreneurial population and racial minorities are

underrepresented in the general population. Weights were calculated so that, when applied, the data are nationally representative of individuals starting businesses (Shaver 2006).

The study consists of four waves, the first conducted between 1998 and 2000. In each wave, respondents were asked to complete both phone interviews and mail

questionnaires; the response rates were higher for the phone interview than the mail questionnaire. For the first wave, there are 830 nascent entrepreneurs at 431 comparison group respondents (1261 total). Some are eliminated because they are improperly classified nascent entrepreneurs in the comparison group sample or operators of existing businesses in the nascent entrepreneur sample. At 12, 24, and 36 months, respondents were asked to complete follow-up interviews and questionnaires. My analyses will only focus on the nascent entrepreneurs, 830 respondents. After cleaning, there are 817 nascent entrepreneurs in wave 1: 715 independent and 102 partial nascent entrepreneurs (for whom part of the business will be owned by an existing organization).

Measures

From the mail questionnaires and phone interviews, respondents were asked to provide detailed information about themselves and their startups. From this information, I was able to construct several measures of status, teams, and entrepreneurial outcomes.

First, I measure achieved or labor force status by considering occupational characteristics, employment history and experience, and level of education. Respondents were asked to give their occupation, which was coded according to 1990 Standard Occupational Classifications (SOC) generated by the United States Census. I assigned SEI Scores (Nakao and Treas 1994) which originated from the 1989 General Social Survey to measure occupational SEI.1 In addition, I used IPUMS (Integrated Public Use Microdata Series) census information to assign sex composition to occupations to determine whether respondents’ occupations are mixed gender, male-typed, or female- typed. I coded occupations with fewer than 35 percent women male-typed, occupations with between 35 and 65 percent women mixed-gender, and occupations with more than 65 percent women female-typed. With regard to labor force attachment, respondents were asked about the major activities they were involved in 12 years prior to the interview (1987 to 1998). These include full- and part-time wage and salary work, education, and self-employment; volunteer work, homemaking, unemployment, disability, discouraged worker, and retirement. From this information, I constructed measurements regarding their labor force status such as whether they have been unemployed or otherwise out of the labor force and whether they have been out of the full-time labor force. My

definitions of out of labor force or out of full-time labor force should not be confused by those of the Bureau of Labor Statistics. For the Bureau of Labor Statistics, labor force participation includes the employed and unemployed and excludes students. My definition excludes unemployed and includes entrepreneurs and students. Individuals reporting that they were not engaged in employment, self-employment, or studies in any

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Although there is some discrepancy between the 1980 and 1990 Standard Occupation Classifications (SOCs), Nakao and Treas (1994) provided information that shows which occupations have been reclassified to enable me to assign occupational SEI scores accurately.

one year were coded as a 1 for ever being out of the labor force, and zero otherwise. Individuals reporting that they were not engaged in full-time employment, self-

employment, or studies in any one year were coded as a 1 for ever being out of the full- time labor force and zero otherwise. Respondents indicated the highest level of education obtained (up to eighth grade, some high school, high school graduate, technical degree, some college, associate’s degree, bachelor’s degree, some graduate work, master’s degree, doctorate degree). From this information, I created an indicator variable for whether they had completed at least a bachelor’s degree. Further, respondents were asked in the mail questionnaire how many courses and how many years of work experience they had in the following business areas: sales/marketing management,

accounting/financial control, production/plant management, personnel/human resource management, transportation/distribution/inventory management, financial and capital management, technological and innovation management, mathematics, and economics. I created measures for how many areas respondents had education and experience in and also separate indicator variables for whether they had financial and accounting

experience and education. Because the mathematics and economics courses could be relatively elementary (high school or lower) I excluded these categories from the measures of business education and experience. Respondents also provided the years of managerial and industry experience, which I logged given the likely diminishing returns of each. In addition, respondents provided the number of startups they had been involved with prior to the interview. I simply created an indicator for whether respondents had any prior startup experience. These measures should provide abundant information regarding respondents’ labor market status.

A variety of individual characteristics often labeled as demographic

characteristics also reflect status, specifically ascribed status. Age is another important status because of age norms, networks, and labor force experience (Lawrence 1988, Riley 1987, Settersten and Mayer 1997). Respondents were asked to give the year of their birth, which I subtracted from the year of the wave 1 interview to generate their age. I also introduced an age-squared term because status often initially rises with age and then begins to fall again with later ages. Respondents were asked their race/ethnicity and gender. I created an indicator variable for whether a respondent was African American or Hispanic and whether a respondent was a woman. Each of these important statuses likely affects entrepreneurial outcomes and startup contributions within resource teams.

Respondents indicated the number of persons in their household who were under the age of 18 and also provided the number of persons in their household under 6. Through these measures, I was able to create an indicator variable for parent and also a measure of the number of children in the household under 6. Importantly, individuals with no children in their home but who have either adult or young children living elsewhere or will be coded the same as those who have never had children. Rather than creating separate variables for “mother” and “mother of young children” I simply run analyses separately for men and women to determine if parenthood and particularly parenting young children have differing effects on men and women.

Teams

Respondents were asked if they would own their business by themselves or with someone else. Those who responded “with someone else” are members of teams. For

respondents on startup teams, I measure the status composition, diversity, and relational composition of teams.

Respondents provided information regarding the status characteristics of their team members. They were asked about their occupations, their years of experience in the industry and their startup experience, their genders, ages, and racial/ethnic background. Therefore, I created status composition measures of teams for average status, maximum status, and status diversity. For average status, I calculated means for the continuous status characteristics of age, occupational SEI, and the log of years of industry experience. For the indicator characteristics of race, gender, startup experience and female-typed occupation, I calculated the proportion of team members having the particular characteristic. To calculate proportions, I needed to generate a team size variable. The variable in the packaged dataset is based on the number of team members for whom respondents provide their gender. In the vast majority of cases, this is an accurate measure. However, once I calculated the proportion measures such as the proportion of individuals with startup experience or a female-typed occupation, I found that on a few occasions, respondents gave responses to those inquiries but not gender, resulting in proportions greater than one. Therefore, I created a new size variable which was the maximum number of non-missing responses for any of the status characteristics. I also used this team size measure as a control. For maximum status, I used the maximum status for continuous variables and an indicator for whether a team had at least one person with a high-status indicator characteristic (male, startup experience, Caucasian, male-typed occupation). The measurement of diversity measures differed for continuous and indicator variables as well. I calculated ranges for continuous status characteristics

and used indicators of whether diversity was present or absent for indicator characteristics.

Respondents were also asked how they knew their team members, allowing me to construct measures of relational composition. Ruef et al. (2003) noted that relations strongly influenced the racial and gender composition of teams, with spouse teams likely to be opposite sex and kin teams likely to be racially homogeneous. Ruef (2003) created a measure of tie strength which he found to significantly influence ownership distributions within startup teams of the PSED. In this measure, kin and spouse teams are scored as 3, friend and colleague ties are scored as 2, and stranger teams are scored as 1.2 Because teams can have more than one relationship type (such as a three person team in which one person is a coworker with the respondent and the other is the spouse), I scored the closest relationship present in the team. I also included an indicator for whether a team had more than one relationship type.

Startup Contributions

In the next chapter, I present my analyses regarding startup contributions within teams, which is a proxy for individual-level power and team level functioning.This analysis will test hypotheses 2, 4, 6, 8-10. Respondents were not asked how often or how well their team cooperated, communicated, or trusted one another. They were not asked how roles or responsibilities were distributed among team members. However,

respondents were asked if their team members provided various types assistance to one another. For each team member (including the respondent), respondents were asked if the team member provided the following types of assistance to the startup: introductions to

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Rather than simply coding the relationships as 1-3, Ruef then used these scores in a more complex formula measuring the Bonacich measure of eigenvector centralization (17). However, I simply code the relationships using the 1-3 scale.

important persons, information or advice, training, access to financial assistance, physical resources, business services, personal services, or other assistance. Then, respondents were asked to choose which, if any, was most important.

Hypothesis 2 focused on how the status characteristics of the individual team members will influence how many contributions they gave and what sorts of assistance they gave.3 For this analysis, respondents’ team members are counted as observations. Because respondents did not give details regarding their team members’ parental status, labor force attachment, or education, I will focus on the following status characteristics: gender, race, age, occupational SEI, occupational sex typing, startup experience, and industry experience to determine which, if any, significantly influence the number and type of contributions made. The individual resource contributions I focus on are introductions, information, training, and personal services. This analysis includes the respondents who said they were members of teams and provided valid responses for the measures under analysis (317 out of 411) plus their team members, for a total of 717.

Hypotheses 4, 6, 8-10 concern the relationship between the composition of the startup team and the nature of startup contributions by team members. Hypothesis 4 concerns average and maximum status, hypothesis 6 concerns diversity, and hypotheses 8-10 concerns relationships among team members. For this analysis, the unit of

observation is the team and therefore I only included the 318 respondents who said they were members of teams and provided valid responses for the measures included in the models. The measures I constructed are the number of different types of assistance

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Given that these answers are from the perspective of respondents, I am actually measuring how many and what kinds of assistance team members are recognized and credited with contributing by the respondents. Team members may give assistance that is not recognized or alternatively may not give assistance and yet receive credit for such contributions.

contributed, the average number of assistance contributions per team member, whether a team member provided any introductions and contacts and as a most important

contribution, information as a most important contribution,4 any training and as a most important contribution, and any personal services and as a most important contribution.

Entrepreneurial Outcomes

Hypotheses 1, 3, 5, 7, and 11-13 concern entrepreneurial outcomes. To measure entrepreneurial outcomes, I consider the conditions of startups twelve months after the initial interview. Respondents were asked to describe their startup as either an operational business, still an active startup, an inactive startup, or an abandoned startup. This measure is a perception-based measure from the perspective of the respondent rather than the definition used in the initial screening of an infant business, one that had three positive months of cash flow. Using this four category response, I run logistic regression on three dependent variables. First, I run analysis on whether respondent abandoned startup activities altogether. Then, I run analysis on whether respondents established operational businesses. Finally, I run analysis on whether respondents are either still actively

involved in their startups or established operational businesses. This final measure could be considered entrepreneurial participation in that, as labor force participation includes those working and those looking for work, entrepreneurial participation includes those operating businesses and those seeking to operate businesses.

Because the minority oversample was initially collected later than the other subsamples, their 12 month follow up interview dates correspond more closely to the 24 month follow-up interviews of the other subsamples. For this reason, in the original data

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Because virtually all respondents on teams had at least one member contribute any information, multivariate analysis on this variable was not possible.

packaging, there are no 12 month follow-up responses for the minority oversample because their responses are included in the 24 month follow-up. Because I am more interested in entrepreneurial outcomes 12 months after the initial interview rather than period effects, I use the responses from the “24 month follow-up” for the minority oversample.

Interaction Effects

I hypothesized several interaction effects between individual status, teams, team composition, and group processes influencing entrepreneurial outcomes. Rather than constructing several interaction terms to test these hypotheses, I run regressions separately for team members and non-team members and for those on team members with high levels of contributions and those not on teams with high levels of contributions. I constructed an indicator variable for the level of contributions provided by first

examining the distribution of the average number of resources provided per team member. I found that the median number of assistance types provided is 3.875.

Therefore, I coded solo entrepreneurs and those on teams with average contributions less than 4 as 0 and those on teams with average resource contributions 4 or greater as 1.

Controls

I control for the log of dollars and hours devoted to the startup. Respondents were asked to estimate how much money and time they had devoted to starting their business at the initial interview. These are important resources for businesses and my analyses test the effects of status and teams on entrepreneurial outcomes net of financial capital and time invested in startups. I also controlled for respondent’s household income and net worth, in 10,000s of dollars and whether respondents were home owners or not.

Respondents were asked to provide their household incomes and net worths, but not all were comfortable doing so. For those who initially refused to respond to these questions, they were asked to answer whether their incomes and net worths were above or below certain thresholds to allow for the most precise level of information possible. Their answers to the income and wealth range questions were used to calculate approximate values. For a detailed discussion of the decision rules for creating these variables, see Kim, Aldrich, and Keister (2004). These measures control for financial resources available to respondents. I also control for industry and technology. First, I created a dummy variable for whether a respondent’s startup was a service or retail business. These are the most popular types of startups and often (although not always) require less capital than startups in manufacturing or wholesale trade, for example. I also controlled for industry-related risk by assigning the one year industry failure rates according to the 1992 Economic Census Characteristics of Business Owners (U.S. Department of Commerce 1997). In addition, I controlled for the level of technology and innovation associated with the respondents’ startups. Respondents were asked whether their product or service was available five years ago (0) or not (1). They were also asked if they would be devoting a substantial amount of resources to research and development (1) or not (0) Finally, they were asked if they considered their startup a high tech startup (1) or not (0). I summed these three responses and divided by three to create an index of innovation and technology (Allen and Stearns 2004). I also controlled for whether their business startup was home-based or not. Finally, I controlled for regional differences by introducing an indicator for south.

Descriptive Statistics

In Table 3.1, I show the weighted means and standard deviations for the variables used in the analysis to test hypothesis 2, that team members with high status will be credited with more resources (with the exception of personal services). As I do in the