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5 Management Science 52(2), pp , 2006 INFORMS 191 Figure 2 Firm Birth Rate (Employer Firms) and Percent of Households with a Home Computer Statistics of U.S. Businesses ( ) and Current Population Surveys ( ) 20.0% 60% 16.7% 50% Firm birth rate 13.3% 10.0% 6.7% Firm birth rate 40% 30% 20% Home computer rate 3.3% Percent of households with a home computer 10% 0.0% % female computer owners are 0.7 percentage points more likely to become entrepreneurs than noncomputer owners. Although this simple comparison of entrepreneurship rates does not control for factors such as income, education, and age, it is suggestive of the direction and size of potential impacts. To control for these factors and others, I first model the entrepreneurship decision. Assume that entrepreneurship is determined by an unobserved latent variable, Y i = X i + C i + u i (3.1) for person i, i = 1 N. Only Y i is observed, which equals 1 if Yi 0, implying that person i chooses to start a business over the following month time interval; Yi equals zero if person i chooses not to start a business over the time interval. X i is a vector of individual, family, and geographical area characteristics measured at the beginning of the time interval, C i is a dummy variable for the presence of a home computer at the beginning of the time interval, and u i is the error term. Only individuals who do not own a business and are not retired or disabled at the beginning of the time interval are included in the sample. Assuming that u i is normally distributed, the data are described by the following probit model: Prob Y i = 1 = X i + C i (3.2) where is the cumulative normal distribution function. Although the normality assumption should only be taken as an approximation, the probit model provides a useful descriptive model for the binary event that an individual starts a business. Tables 2A and 2B report estimates from probit regressions for the probability of becoming an entrepreneur over a month time period. All specifications include controls for race, immigrant status, age, marital status, number of children in the household, education level, family income, region of the country, central city status, home ownership, the year of the CIUS (1997, 1998, 2000, or 2001), and the length of the time interval (12 15 months). All controls are measured at the time of the first survey (i.e., in the CIUS). Sample means and standard deviations for most variables are reported in Appendix A in the online appendix, available at informs.org/ecompanion.html. Estimates for men, which are reported in Table 2A, are discussed first. Specification 1 of Table 2A reports estimates excluding the home computer variable. Blacks, Hispanics, and Asians have lower entrepreneurship rates, and immigrants have higher rates, all else equal. Men who are older (through age 45), married, and have at least two children have higher rates of entrepreneurship. Higher levels of education are associated with larger probabilities of starting a business. In contrast, the relationship between family income and entrepreneurship is ambiguous. Most of the coefficients on the family income dummies are statistically insignificant. Another surprising result is that home ownership does not have a positive and statistically significant effect on entrepreneurship. One might expect that home ownership represents a proxy for wealth, and there is substantial evidence suggesting that assets are an important determinant of entry into self-employment (see Holtz-Eakin et al. 1994, Dunn and Holtz-Eakin 2000, Fairlie 1999 for a few recent examples). Specification 2 includes the dummy variable indicating whether the individual had access to a home computer measured at the time of the first survey.

6 192 Management Science 52(2), pp , 2006 INFORMS Table 2A Probit Regressions for Entrepreneurship Men, Current Population Surveys ( ) Specification Explanatory variables (1) (2) (3) (4) (5) Black Latino Asian Immigrant Age Age squared High school graduate Some college College graduate Graduate school Family income: \$10,000 to \$15, Family income: \$15,000 to \$20, Family income: \$20,000 to \$25, Family income: \$25,000 to \$30, Family income: \$30,000 to \$35, Family income: \$35,000 to \$40, Family income: \$40,000 to \$50, Family income: \$50,000 to \$60, Family income: \$60,000 to \$75, Family income: more than \$75, Home owner Access to a home computer Marginal effect Home computer in Home computer in Home computer in Access to the Internet at home Mean of dependent variable Log-likelihood value Sample size 39,972 39,972 39,972 39,972 39,972 Notes. (1) See notes to Table 1. (2) All independent variables are measured in the first year surveyed. (3) All equations also include a constant, number of children, dummy variables for Native American, marital status, census divisions, central city status, year effects, and length of time interval effects. (4) Specification 3 relaxes the hours restriction on owning a business in the second year surveyed.

7 Management Science 52(2), pp , 2006 INFORMS 193 Table 2B Probit Regressions for Entrepreneurship Women, Current Population Surveys ( ) Specification Explanatory variables (1) (2) (3) (4) (5) Black Latino Asian Immigrant Age Age squared High school graduate Some college College graduate Graduate school Family income: \$10,000 to \$15, Family income: \$15,000 to \$20, Family income: \$20,000 to \$25, Family income: \$25,000 to \$30, Family income: \$30,000 to \$35, Family income: \$35,000 to \$40, Family income: \$40,000 to \$50, Family income: \$50,000 to \$60, Family income: \$60,000 to \$75, Family income: more than \$75, Home owner Access to a home computer Marginal effect Home computer in Home computer in Home computer in Access to the Internet at home Mean of dependent variable Log-likelihood value Sample size 47,261 47,261 47,261 47,261 47,261 Note. See notes to Table 2A.

10 196 Management Science 52(2), pp , 2006 INFORMS estimated. This model is equivalent to an instrumental variable or two-stage least squares model and is preferred when both the dependent variable and endogenous variable are binary. Similar to (3.1), assume that home computer ownership is determined by an unobserved latent variable, C i = X i + Z i + i (3.3) where only C i equal to 0 or 1 is observed, Z i is a vector of variables that are not included in (3.1), and i is the error term. In this case, u i and i are distributed as bivariate normal with mean zero, unit variance, and = Corr u i i. The bivariate probit model is appropriate when 0 (see Greene 2003 for further discussion). In practice, the choice of Z i is difficult. In this application, however, there are two potential exclusion restrictions. First, I use information on the use of the Internet outside the home among all household members. In particular, I create a dummy variable that equals one if anyone else living in the individual s household uses the Internet at work. Internet use at work by another household member should satisfy the two necessary properties of a valid instrumental variable it affects the probability of purchasing a computer, but does not affect entrepreneurship (after controlling for other factors). There exists a strong correlation between using a computer at work by a household member and computer ownership by that household (U.S. Department of Commerce 2002). In addition, we do not expect the use of the Internet at work by another household member to have a strong direct effect on whether the individual becomes an entrepreneur after controlling for family income, education, and marital status. Internet use at work by another household member may be associated with higher family income, but this effect is already controlled for and, as indicated above, does not have a clear positive or negative effect on entrepreneurship. It is useful to examine the correlation between these variables and the probability of entrepreneurship in my sample (see Appendix B in the online appendix). For men, the probability of entrepreneurship is essentially the same for those who live in households where someone else uses the Internet at work and for those who do not live in households where someone else uses the Internet at work. For women, however, the probability of entrepreneurship appears to be somewhat higher for individuals who have household Internet users compared to those who do not. Another exclusion restriction used in the bivariate probit model is whether a teenager is present in the household. Teenagers have the highest probability of computer use for all age groups (U.S. Department of Commerce 2002). Estimates from all four CIUS files included in this analysis indicate that teenagers (ages 10 17) have a high probability of living in a household with a home computer (see the figure in the online companion). As expected, the probability of having a home computer is much higher for this age group than for younger children. The probability also declines substantially after age 17 and is relatively low for young adults. On the other hand, the presence of a teenager is not expected to have a large independent effect (after controlling for the number of children and marital status) on the probability of entrepreneurship among adults in the household. Estimates reported in Appendix B also indicate that the presence of teenagers is uncorrelated with the probability of entrepreneurship. Entrepreneurship rates are only slightly lower for men and women who have teenage children compared to those who do not have teenage children. Estimates from the bivariate probit model for the probability of entrepreneurship and having a home computer are reported in Tables 3A and 3B. I first discuss the results for men (reported in Table 3A). The first column reports the coefficients for the probability of owning a computer. As expected, education is an important determinant of owning a home computer. The probability of owning a home computer increases substantially with the individual s education level. Education may be a proxy for wealth or permanent income and have an effect on the budget constraint or may have an effect on preferences for computers through pure tastes, exposure, perceived usefulness, or conspicuous consumption. Family income is also important in determining who owns a home computer. The relationship between the home computer probability and income is almost monotonically increasing across the listed categories. It is likely to be primarily due to its effect on the budget constraint, however, it may also be due to its effect on preferences. Related to these variables, home ownership increases the probability of having access to a home computer. The effect, however, is relatively small compared to the effects of education and family income. Race and ethnicity are also important determinants of computer ownership. Blacks, Latinos, and Native Americans have lower probabilities of owning a home computer than do whites. In addition to these control variables, marital status, the number of children, region, central city status, and the year of the survey also have statistically significant effects on the home computer probability. Both instrumental variables have positive and statistically significant coefficients in the home computer equation. The coefficients on these variables imply large effects on the probability of having a home computer. The presence of another household member

11 Management Science 52(2), pp , 2006 INFORMS 197 Table 3A Bivariate Probit Regressions for Entrepreneurship and Home Computer Men, Current Population Surveys ( ) Specification (1) (2) Dependent variables Computer Entrepreneurship Computer Entrepreneurship Explanatory variable Black Latino Asian Immigrant Age Age squared High school graduate Some college College graduate Graduate school Family income: \$10,000 to \$15, Family income: \$15,000 to \$20, Family income: \$20,000 to \$25, Family income: \$25,000 to \$30, Family income: \$30,000 to \$35, Family income: \$35,000 to \$40, Family income: \$40,000 to \$50, Family income: \$50,000 to \$60, Family income: \$60,000 to \$75, Family income: more than \$75, Home owner Access to a home computer Marginal effect Other household member uses the Internet at work Teenager present in household Other household member uses the Internet outside the home (0.0804) (0.0975) Mean of dependent variable Sample size 39,972 30,570 Note. (1) See notes to Table 2A. (2) Specificiation 2 includes data from 1997, 1998, and 2001 only.

12 198 Management Science 52(2), pp , 2006 INFORMS Table 3B Bivariate Probit Regressions for Entrepreneurship and Home Computer Women, Current Population Surveys ( ) Specification (1) (2) Dependent variables Computer Entrepreneurship Computer Entrepreneurship Explanatory variable Black Latino Asian Immigrant Age Age squared High school graduate Some college College graduate Graduate school Family income: \$10,000 to \$15, Family income: \$15,000 to \$20, Family income: \$20,000 to \$25, Family income: \$25,000 to \$30, Family income: \$30,000 to \$35, Family income: \$35,000 to \$40, Family income: \$40,000 to \$50, Family income: \$50,000 to \$60, Family income: \$60,000 to \$75, Family income: more than \$75, Home owner Access to a home computer Marginal effect Other household member uses the Internet at work Teenager present in household Other household member uses the Internet outside the home (0.0948) (0.1027) Mean of dependent variable Sample size 47,261 35,995 Note. See notes to Table 3A.

13 Management Science 52(2), pp , 2006 INFORMS 199 who uses the Internet at work increases the probability of having a home computer by , which is roughly equal to the effect of increasing family income from less than \$10,000 to the \$30,000 \$35,000 level. 13 The presence of a teenager has an even larger effect on the probability of owning a home computer. All else equal, if a teenager is present in the household, the probability of having a home computer is higher. The second column reports coefficients for the probability of entrepreneurship. The coefficients on the control variables in the bivariate probit model are very similar to those for the probit model. The coefficient estimate on home computer is also similar, but is now slightly larger. The point estimate implies that the presence of a home computer increases the probability of entrepreneurship by The main difference between the bivariate probit and probit results is that the coefficient estimate is no longer close to being statistically significant at conventional levels. The CPS CIUS files contain additional information on Internet use outside the home. In particular, information exists on Internet use anywhere outside the home for every survey except Using a smaller sample, I estimate a bivariate probit model that replaces the dummy variable for other household member s use of the Internet at work with a dummy variable for other household member s use of the Internet anywhere outside the home. Specification 2 reports estimates. The coefficient on the nonhome use of the Internet by another household member is large, positive, and statistically significant. The coefficient on the home computer increases to , but remains statistically insignificant. Overall, for men, the bivariate probit results do not provide conclusive evidence suggesting that home computers increase entrepreneurship because of the lack of statistical significance, but they do not overturn the findings from the probit regressions presented above. I now turn to the results for women, which are reported in Table 3B. As expected, the determinants of computer ownership are generally similar between women and men. Computer ownership increases with education, family income, and home ownership, but is lower among blacks, Latinos, and Native Americans, all else equal. The coefficients on the instruments are also fairly similar. In both specifications, the included instruments have large, positive, 13 Unfortunately, work use of computers is not available in 1997 and Using a sample of children from the 2001 CPS, Fairlie (2005), however, finds that Internet use at work by parents has a larger effect on the probability of computer ownership than computer use at work among parents. Communication and information retrieval uses of computers at work may have a stronger association with purchasing home computers than other uses, such as appointment scheduling, database entry, and production. and statistically significant coefficients. In Specification 1, the coefficient estimate on home computer is somewhat larger than the coefficient estimate in the comparable probit specification. The coefficient increases from to The bivariate probit coefficient estimate, however, is no longer statistically significant at the = 0 05 level. Using the smaller time period in Specification 2, the coefficient on home computer is even larger. It is now statistically significant and implies that computer ownership increases the probability of entrepreneurship by Exclusion Restriction Issues Estimates from the bivariate probit also allow one to test whether a correlation exists between the unobserved factors affecting home computer ownership and entrepreneurship. Formal tests of the hypothesis that = 0 reveal consistent results across the four specifications. For all specifications, especially for men, the test statistics are substantially lower than the chi-squared critical value suggesting that the unobserved factors are uncorrelated. Although these results provide evidence that the original probit estimates are consistent and that estimation of the bivariate probit is unnecessary, it is useful to further explore the validity of the exclusion restrictions for completeness. Although the estimates reported in Appendix B provide evidence on whether the excluded variables are uncorrelated with the probability of entrepreneurship, the true test is whether they are uncorrelated with unobserved factors affecting the probability of entrepreneurship after controlling for other factors (i.e., u i ). One method of exploring this issue is to estimate a probit model for entrepreneurship that includes the variables listed in Table 2 and the excluded variables. Although not reported, I generally find small and statistically insignificant coefficient estimates on the excluded variables for both men and women. The only exception is that I find a negative and statistically significant coefficient on the presence of teenagers using the shorter time period for men. Although this is not a formal test of the validity of the instruments, it suggests that Internet use at work or outside the home by other household family members and the presence of a teenager in the household generally do not have a large effect on the probability of entrepreneurship after controlling for other factors. To address concerns that one of the excluded variables is problematic, I estimate bivariate probits that include the presence of teenagers and other household member uses the Internet at work separately. Estimates are reported in Table 4, Parts A and B. For men, the coefficients on home computer increase, but remain statistically insignificant. For women, the coefficient on home computer is smaller than the bivariate probit estimate, but larger than the original probit

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