Entrepreneurship and innovation are believed to be the driving forces of the US economy. Many new startups rely on venture investors to acquire adequate capital and make it possible to start and run their businesses. In addition to capital, early-round investors such as venture capitalists also provide expertise, knowledge of management and product markets, and essential resources to the entrepreneurs, thus playing a crucial role in their growth. This dissertation aims to understand several essential questions on venture capital financing, entrepreneurship, and other venture investments.The first chapter evaluates the network effects in venture capital financing in the US market and provides possible channels. We conduct two empirical studies and estimate the positive network effects with rigorous addresses on the endogeneity problem. First, we simultaneously model the endogenous network formation and outcome models with unobserved confounding variables incorporated in both models. The estimation result shows a positive network effect of 0.127 when the outcome of interest is the performance of the venture capitalists, measured by the success rate defined as the ratio between the number of successful deals over the total number of deals this venture capitalist ever made. The result indicates that for a venture capitalist in the network, a one percentage point increase in the average success rate of its peers will lead to a 0.127 percent increase in its success rate. We further decompose the observed VC network into two parts, one within the same industry sector and one across different sectors, and estimate their heterogeneous network effects. We find that peer effects in both networks are significantly positive, indicating not only does the sharing of information within an industry matter, but the sharing of resources and expertise also adds value to the connections across different industries. Our second study uses the quasi-experimental design, the generalized difference-in-differences method, to more intuitively illustrate the network effects by estimating the treatment effect of a venture capitalist’s extremely successful event (an IPO) on the future performance of their connected venture capitalists. We find that once an IPO occurs in a venture capitalist’s portfolio, more startups in their peers’ portfolios will receive new rounds of funding and achieve successful exits, and fewer startups will go bankrupt. In addition to these empirical studies, we build a theoretical model on the decision-making processes of venture capitalists to better understand the network effects and demonstrate the benefits and costs of syndication compared with standalone investments. We also test several model implications using our data. The syndicated deals have a 9.65% higher success rate, a 15.3% lower bankruptcy rate, and a 12.3% higher internal rate of return compared with standalone deals, aligning with the empirical implications of our theoretical model. In the second chapter, we study the widespread common ownership in venture capital financing. Common ownership describes the phenomenon that competitors in the same industry share the same investors. Venture capitalists strategically build and actively manage their portfolio of startups, leading to the possibility that they invest simultaneously in multiple startups in the same industry and share information and ideas among them. While seemingly beneficial for startups to be commonly owned, the “horse race” investment strategy may hurt the startups at the same time. We evaluate the effect of common ownership on startups utilizing a matched-pair design as our identification strategy. Our results show that when a successful financing event occurs to a startup, its peers in the same common ownership pool have a 1.31% higher probability of getting new rounds of funding within 180 days, 2.4% higher within 365 days, and 3.37% higher within 730 days, compared with those that are not commonly owned. Moreover, the effects of common ownership are heterogeneous across industries. Although conventional wisdom regards equity as the pivotal financing vehicle for new firms, in recent years we have observed unexpectedly active debt financing in the early-round startup financing market, considering relatively low rates of return and extremely high risks. The third chapter builds a theoretical model to study signaling in venture debt and capital and explains the seemingly puzzling existence of venture debt. We find that startups use venture debt as a good signal for financing. The cost of due diligence for venture capital firms is sufficiently high that they prefer to utilize this signal instead of investigating. We document and test four empirical implications of our model. First, startups with venture debt can get next-round funding faster than those without venture debt due to the signaling effect of venture debt. On average, a startup with venture debt reaches the next round of funding about a hundred days faster than those without venture debt. Second, startups with venture debt generally have significantly better long-term performance measured by their exit status. They have a lower probability of failure and a higher probability of going public. Third, conditional on the startups getting their next round of funding, those with venture debt have worse long-run performance compared to a counterfactual world without venture debt. Finally, the signaling effect is more substantial when venture capital investors suffer from more severe asymmetric information problems. Our empirical results align with the model predictions and are robust to various specifications.

This dissertation consists of two chapters. The first one deals with the information content of bond prices in private label securitization markets. The performance of a security backed by a pool of loans is affected by default correlation, and not only the probability of default. I imply default correlation from the market price of collateralized mortgage obligations. Implied correlations are informative about subsequent bond downgrades, but this information content depends on the quality of documentation on the underlying loans. Correlations implied from junior tranches are no more informative than those of AAA tranches for "low-doc" deals, and the latter no less informative than the former for "full-doc" deals. Errors in computing default correlations were not exclusive to AAA investors.

The second chapter in this dissertation deals with the structural estimations of utility-based models in a setting of economic decision-making. Dropping the assumption that all individuals are all self-regarding we develop a model of utility maximization under social preferences. We use data from a common pool resource (CPR) game run in the field (1,095 subjects) to estimate a structural model including preferences for selfishness, altruism, reciprocity and equity, identifying preference types using a latent class logit model. Exogenous determinants of type are examined such as socio-economic characteristics, perceptions on the CPR, perceived interest in cooperation among the community, whether the participant does volunteer work and whether the CPR is the household main economic activity of the household. A competing explanation of deviations from Nash equilibrium is the existence of a cognitive factor: the construction of a best reply might make rational expectations about other players’ mistakes (e.g. quantal response equilibrium). We do not find evidence for cognitive heterogeneity. Choice prediction based on types is robust out of sample.