Two decades ago an influential article documented the alarming disparities that existed in access to computers and the Internet between African-Americans and whites (Hoffman and Novak, 1998). Using the latest U.S. Census Bureau/Bureau of Labor Statistics data on computer and Internet access, I find that the “digital divide” has not been bridged and remains as large as it was two decades ago. African-Americans and Latino-Americans are less likely to use the Internet on smart phones, computer, tablets or other devices than are whites. A statistical decomposition analysis reveals that income and education inequalities are the leading causes of the disparities in access to technology. The findings have implications for policies that subsidize broadband to low-income families.

Job creation is one of the most important aspects of entrepreneurship, but we know relatively little about the hiring patterns and decisions of startups. Longitudinal data from the Integrated Longitudinal Business Database (iLBD), Kauffman Firm Survey (KFS), and the Growing America through Entrepreneurship (GATE) experiment are used to provide some of the first evidence in the literature on the determinants of taking the leap from a non-employer to employer firm among startups. Several interesting patterns emerge regarding the dynamics of non-employer startups hiring their first employee. Hiring rates among the universe of non-employer startups are very low, but increase when the population of non-employers is focused on more growth-oriented businesses such as incorporated and EIN businesses. If non-employer startups hire, the bulk of hiring occurs in the first few years of existence. After this point in time relatively few non-employer startups hire an employee. Focusing on more growth- and employment-oriented startups in the KFS, we find that Asian-owned and Hispanic-owned startups have higher rates of hiring their first employee than white-owned startups. Female-owned startups are roughly 10 percentage points less likely to hire their first employee by the first, second and seventh years after startup. The education level of the owner, however, is not found to be associated with the probability of hiring an employee. Among business characteristics, we find evidence that business assets and intellectual property are associated with hiring the first employee. Using data from the largest random experiment providing entrepreneurship training in the United States ever conducted, we do not find evidence that entrepreneurship training increases the likelihood that non-employers hire their first employee.

A common finding in the entrepreneurship literature is that business creation increases in recessions. This counter-cyclical pattern is examined by separating business creation into two components: “opportunity” and “necessity” entrepreneurship. Although there is general agreement in the previous literature on the conceptual distinction between these two factors driving entrepreneurship, there are many challenges to creating a definition that is both objective and empirically feasible. We propose an operational definition of opportunity versus necessity entrepreneurship using readily available nationally representative data. We create a distinction between the two types of entrepreneurship based on the entrepreneur’s prior work status that is consistent with the standard theoretical economic model of entrepreneurship. Using this definition we document that “opportunity” entrepreneurship is pro-cyclical and “necessity” entrepreneurship is counter-cyclical. We also find that “opportunity” vs. “necessity” entrepreneurship is associated with the creation of more growth-oriented businesses. The operational distinction proposed here may be useful for future research in entrepreneurship.

A large body research shows a positive relationship between wealth andentrepreneurship and interprets the relationship as providing evidence of liquidity constraints.Recently, however, the liquidity constraint interpretation has been challenged because of thefinding that the relationship between business entry rates and assets is flat throughout most of theasset distribution and only rises dramatically after this point (Hurst and Lusardi 2004). Wereexamine the liquidity constraint hypothesis in three ways. First, we demonstrate that examiningthe relationship separately for those who experience a job loss and those who do notreveals generally increasing entry rates through the wealth distribution for both groups. Basedon the entrepreneurial choice model of Evans and Jovanovic (1989), these two groups facedifferent incentives, and thus have different solutions to the entrepreneurial decision. We alsofind evidence of a stronger relationship between entrepreneurship and a different measure ofwealth – net housing equity – for the two groups. Second, we examine the liquidity constrainthypothesis using a two-period simulation model that extends the Evans and Jovanovic (1989)model. The model shows how exogenous wealth shocks can be used to accurately identify thepresence of liquidity constraints even allowing for endogenous saving and correlated abilities.Third, we provide new evidence from matched Current Population Survey (1993-2004) data tostudy whether changes in housing prices affect self-employment entry. We find thathousing appreciation measured at the MSA-level is a significantly positive determinant of entryinto self-employment after controlling for changes in local economic conditions and otherfactors. Our estimates indicate that a 10 percent annual increase in housing equity increases themean probability of entrepreneurship by 17 percent and that the effect is not concentrated at theupper tail of the distribution. These estimates are not sensitive to controlling for pre-existingtrends in housing prices suggesting that the results are not being driven by expected localeconomic growth.

Concerns over the perceived negative impacts of computers on social development among children are prevalent but largely uninformed by plausibly causal evidence. We provide the first test of this hypothesis using a large-scale randomized control experiment in which more than one thousand children attending grades 6–10 across 15 different schools and 5 school districts in California were randomly given computers to use at home. Children in the treatment group are more likely to report having a social networking site, but also report spending more time communicating with their friends and interacting with their friends in person. There is no evidence that computer ownership displaces participation in after-school activities such as sports teams or clubs or reduces school participation and engagement.