Increasingly, local governments compete by offering substantial subsidies to industrial plants to locate within their jurisdictions. This paper uses a novel research design to estimate the local consequences of successfully bidding for an industrial plant, relative to bidding and losing, on labor earnings, public finances, and property values. Each issue of the corporate real estate journal Site Selection includes an article titled "The Million Dollar Plant" that reports the county where a large plant chose to locate (i.e., the 'winner'), as well as the one or two runner-up counties (i.e., the 'losers'). We use these revealed rankings of profit-maximizing firms to form a counterfactual for what would have happened in the winning counties in the absence of the plant opening. We find that the plant opening announcement is associated with a 1.5% trend break in labor earnings in the new plant's industry in winning counties, as well as increased earnings in the same industry in counties that neighbor the winner. Further, there is modest evidence of increased expenditures for local services, such as public education.

Property values may provide a summary measure of the net change in welfare, because the costs and benefits of attracting a plant should be capitalized into the price of land. If the winners and losers are homogeneous, a simple model suggests that any rents should be bid away. We find a positive, relative trend break of approximately 1.1-1.7% in property values. Since the winners and losers have similar observables in advance of the opening announcement, the property value results may be explained by heterogeneity in subsidies from higher levels of government (e.g., states) and/or systematic underbidding. Overall, the results undermine the popular view that the provision of local subsidies to attract large industrial plants reduces local residents' welfare.

We estimate the effect of maternal education on birth outcomes using data from the Vital Statistics Natality files for 1970 to 1999. We also assess the importance of four potential channels through which maternal education may improve birth outcomes: use of prenatal care, smoking behavior, marriage, and fertility. In an effort to account for unobserved characteristics of women that could induce spurious correlation, we pursue two distinct empirical strategies. First, we construct panel data by linking women in different years of the Vital Statistics records and examine the effects of changes in education on changes in birth outcomes. Second, we have compiled a new data set on openings of two and four year colleges between 1940 and 1990. We use data about the availability of colleges in the woman's county in her 17th year as an instrument for maternal education Our findings using the two approaches are similar. Higher maternal education improves infant health, as measured by birthweight and gestational age. It also increases the probability that a new mother is married, reduces parity, increases use of prenatal care, and reduces smoking, suggesting that these are important pathways for the ultimate effect on health.

In this thematic dissertation, which is divided in three chapters, I discuss new evidence

and insights about knowledge spillovers, that is the process by which new productive ideas benefit individuals and organizations that did not originally devise them. The general theme of the dissertation is the empirical search for one specific micro-level mechanism of information diffusion: that is, social interactions between inventors who are connected in a network of professional collaborations. In the first and second chapter I examine how inventor-level networks function as a channel of R&D spillovers across firms. The two chapters separately introduce two different and novel methodologies to estimate spillover effects in this setting. Both methods stem, however, from a shared theoretical framework, and the resulting econometric estimates are similar across the two approaches. In the third chapter I analyze a different but related issue: that is how geographical proximity facilitates knowledge exchange between inventors, in particular between “superstar” scientists and their coauthors. In the remainder of this abstract I provide a brief summary of each of the three chapters.

The first chapter illustrates a new methodology of quantitatively assessing R&D spillovers across firms in a reduced form fashion. As mentioned, I directly test the hypothesis that interactions between inventors and scientists of different organizations drive knowledge spillovers. To this end, I construct a network of publicly traded companies where each link is a function of the relative proportion of any two firms’ inventors who have patent coauthors in both said organizations. I use this measure of connection to weigh the impact of R&D performed by each firm on the innovation and productivity outcomes of its neighbors in the network. An empirical concern is that the resulting estimates may reflect, rather than genuine spillover effects, some unobserved, simultaneous drivers of both R&D and firm performance, which are common to closely connected firms. By formally modeling the strategic dependence of R&D choices in the network I characterize the conditions under which specific instrumental variables, based on the R&D choices of more distantly related firms (“indirect friends”), solve the problem. Empirical results show that substantial effects of external R&D on firm performance and innovation are identified by this approach. I calculate the resulting marginal social return of R&D to be approximately 24% of the corresponding marginal private return.

In the second chapter I adopt a more structural approach to estimate the parameters of

the model from the first chapter. Instead of quantifying spillovers in terms of their ultimate

effects on firm-level productivity and innovation outcomes, I estimate the effect of connected firms’ R&D in stimulating private R&D investment strategies. On the basis the equilibrium conditions of the R&D investment game, formulated under general assumptions, I establish a set of moment conditions of both the first and second order, relative to equilibrium R&D, that are exploited for estimation. The model separately identifies spillover effects and the variance of common shocks thanks to a zero conditional covariance restriction. Its intuition is summarized as follows: for any two firms that are only indirectly related in the network, their reciprocal strategic dependence is removed by holding constant the choices of their intermediate links. Thus, any residual cross-correlation in R&D must be driven by shared external factors, whose size can be estimated. The empirical results evidence that the R&D of connected firms positively stimulates private investment with an elasticity of about 0.2.

In the third and final chapter I analyze spillover effects on the production of patents following episodes in which superstar inventors relocate to a new city. In particular, in order to determine whether local externalities have a restricted network dimension or a wider spatial breadth, I estimate changes in patterns of patenting activity for two different groups of inventors: the restricted group of coauthors of the superstar, and all other inventors from one specific urban area. The analysis is performed for both the locality where the superstar moves and for the one that is left. I restrict the attention to outcome measures of patent output that exclude any joint work with the superstar, so to isolate spillovers from complementarity effects. The results from the event study evidence a large and persistent positive effect on the coauthors of the superstar who reside in the city of destination (averaging about 0.1 more patents per inventor each year), and a negative trend affecting those who live in the locality of departure. These effects increase with the relative ranking of the superstar in the patent distribution. Conversely, no city-wide spillover effect can be attested, offering little support to place-based policies aimed at generating a positive influx of highly skilled individuals.

Little is known about the mechanisms underlying the transfer of economic status between generations. This paper addresses the question of whether inter-generational correlations in health contribute to the perpetuation of economic status. We examine inter-generational correlations in birth weight, a key indicator of the health of newborns that we link to future educational attainment and earnings using a unique data set based on California births from 1960s to the present. We use names and birth dates to link the records of mothers and children. We also identify mothers who are siblings. We show that there is a strong intergenerational correlation in the birth weight of mothers and children, but that a measure of household income at the time of the mother's birth is also predictive of low birth weight and that there is an interaction between maternal low birth weight and poverty in the production of low birth weight. Together these findings suggest that intergenerational correlations in health could play a role in the intergenerational transmission of income. Parent's income affects child health, and health at birth affects future income.

This dissertation studies the role of urban policies in shaping local housing markets, local labor markets, and transportation decisions. Chapter 1 uses tax records to document the first available evidence on the short-run response of financial capital to the Opportunity Zone (OZ) program, a federal place-based policy that provides tax incentives for capital investments in low-income neighborhoods. My coauthor and I document how OZ investments are spatially concentrated, flow towards areas with improving trends, and are primarily claimed by individuals at the 99th percentile of the national income distribution. In Chapter 2, I explore how real estate investment and home values respond to OZ tax credits. I find that targeted neighborhoods see a significant increase in new development and that local home values appreciate. Nearby areas see an increase in investment as well. Through a spatial-equilibrium model, I find that while the program as implemented had net benefits in terms of home value appreciation, alternative designations of neighborhoods for the tax credit could have substantially improved the program's equity and efficiency. In Chapter 3, my coauthor and I investigate the long-run effects of converting buildings to high-end condominiums on local home prices, demographics, and new business entry. We find that owners who are able to change their building’s legal status to condominium tend to see their property values increase and are more likely to invest in renovations and rent out their units before selling them. Following these conversions, the values of adjacent buildings increase and the surrounding neighborhood gentrifies. Finally, in Chapter 4, my coauthors and I study whether ride-hailing complements or substitutes public transportation by examining the response of Uber ridership to rail expansions. We find that a new station significantly increases Uber ridership nearby, and that this effect decays to zero farther away. In studying the economic effects of urban policies, the chapters of my dissertation point to the value of a layered approach: using granular spatial data; adjusting analyses for neighborhood particularities to estimate the causal impacts of a policy and to account for its indirect effects on nearby areas; and delineating the manner in which public policy effects vary with neighborhood characteristics.

We estimate the effect of high school graduation on participation in criminal activity accounting for endogeneity of schooling. We begin by analyzing the effect of high school graduation on incarceration using Census data. Instrumental variable estimates using changes in state compulsory attendance laws as an instrument for high school graduation uncover a significant reduction in incarceration for both blacks and whites. When estimating the impact of high school graduation only, OLS and IV estimators estimate different weighted sums of the impact of each schooling progression on the probability of incarceration. We clarify the relationship between OLS and IV estimates and show that the 'weights' placed on the impact of each schooling progression can explain differences in the estimates. Overall, the estimates suggest that completing high school reduces the probability of incarceration by about .76 percentage points for whites and 3.4 percentage points for blacks. We corroborate these findings using FBI data on arrests that distinguish among different types of crimes. The biggest impacts of graduation are associated with murder, assault, and motor vehicle theft. We also examine the effect of drop out on self-reported crime in the NLSY and find that our estimates for imprisonment and arrest are caused by changes in criminal behavior and not educational differences in the probability of arrest or incarceration conditional on crime. We estimate that the externality of education is about 14-26% of the private return to schooling, suggesting that a significant part of the social return to education comes in the form of externalities from crime reduction.

The first chapter of the dissertation examines the learning process that economic agents use to update their expectation of an uncertain and infrequently observed event. The standard Bayesian updating model is restrictive in that it reflects the strong neo-classical assumption that economic agents efficiently incorporate new information with all available information when updating beliefs. I consider the case of flooding and estimate the effect of first-hand experience on flood insurance take-up. I compile a new nation-wide panel dataset of large regional floods and flood insurance policies in the US. First, I show that flood insurance take-up in flooded communities increases by 9% after a flood and then steadily declines, fully dissipating after 9 years. Floods do not affect take-up in geographically neighboring non-flooded communities unless these communities are in the same media market. The take-up rate in non-flooded communities that share a media market with a flooded community is one-third as large as in flooded communities. I interpret this evidence using the standard Beta-Bernoulli Bayesian learning model and a Beta-Bernoulli model that includes a forgetting/first-hand experience parameter. I find that the standard Bayesian model can not explain both the spike in insurance in the year of a flood and the decay rate of this effect on insurance take-up in the years after the flood. I conclude that the evidence is most consistent with a Bayesian model augmented with a forgetting/first-hand experience parameter.

The second chapter of my dissertation examines the causal link between localized exposure to hazardous waste pollutants from motor vehicle exhaust and adverse human health outcomes for newborns. I explore whether an exogenous event--the 1994 Northridge Earthquake--can be used as a quasi-experiment to test how birth outcomes change from a sudden and unexpected increase in pollution. The Northridge Earthquake closed down portions of four busy highways in Los Angeles, CA for periods of 1-6 months. The highway traffic was diverted onto secondary roads that previous to the earthquake had a much lower traffic volume. The paper focuses on two health outcomes for newborns: birth weight and gestation period. Infants born preterm or with low birth weight are less likely to survive infancy, more likely to suffer from childhood illness, and have lower future earnings. Overall the results of this study are inconclusive due to the relatively small number of new births included in the sample design. However, the results do suggest that a mother's race, age, and level of education are more important than proximity to a highway. Being a minority race, a teenage mother, or not having any college education are correlated with lower birth weight. The size of these correlations are approximately an order of magnitude larger than the point estimates for the effect of living in close proximity to a road with heavy traffic.

The third chapter of the dissertation uses the housing market to develop estimates of the local welfare impacts of Superfund sponsored clean-ups of hazardous waste sites. We show that if consumers value the clean-ups, then the hedonic model predicts that they will lead to increases in local housing prices and new home construction, as well as the migration of individuals that place a high value on environmental quality to the areas near the improved sites. We compare housing market outcomes in the areas surrounding the first 400 hazardous waste sites chosen for Superfund clean-ups to the areas surrounding the 290 sites that narrowly missed qualifying for these clean-ups. We find that Superfund clean-ups are associated with economically small and statistically indistinguishable from zero local changes in residential property values, property rental rates, housing supply, total population, and the types of individuals living near the sites. These findings are robust to a series of specification checks, including the application of a regression discontinuity design based on knowledge of the selection rule. Overall, the preferred estimates suggest that the local benefits of Superfund clean-ups are small and appear to be substantially lower than the $43 million mean cost of Superfund clean-ups.