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This dissertation consists of two essays on international trade and trade policy, focusing on a quantitative evaluation of the causes and consequences of trade barriers and economic integration. In the first essay, I examine the role of a widely-used discriminatory trade barrier in shaping dynamic trade patterns of exporters across a range of affected and unaffected export markets. In the next chapter, I explore the heterogeneous economic determinants impacting the formation of economic integration agreements among countries, paying specific attention to the role of bilateral migration flows. The first chapter estimates the dynamic trade effects of temporary trade barriers (TTBs), a form of targeted trade barrier widely used by members of the World Trade Organization in response to alleged unfair trade practices. TTBs like antidumping (AD) have been shown to have large and persistent effects on trade flows between countries, but there is mixed evidence on the effect of these tariffs on trade to unrelated markets in part driven by unique institutional features that complicate identification. In this essay, we revisit these classic TTB questions with a focus on AD policy using publicly available product-level trade data and a dynamic difference-in-differences framework, paying specific attention to trends in export growth. We find qualitatively different trade effects when accounting for growth effects that suggest AD investigations are associated with global reductions in within-product trade. We provide evidence that these reductions are not primarily driven by policy-related chilling effects, and argue scale economies in exporting may play a large part. We also identify a significant amount of heterogeneity in the third market trade effects of AD investigation across export markets. Our findings suggest the aggregate impact of AD policy on global trade flows of exporters is potentially large and complicated due to interdependence of export markets and long run dynamics. The second essay investigates the formation of economic integration agreements (EIAs). EIAs are widespread multilateral agreements between countries with a variety of characteristics based on the level of integration, ranging from preferential trade agreements to economic unions. However, little research documents heterogeneity in the economic determinants of EIA formation beyond the classical forces of trade creation and diversion. Further, little previous work has explored the role of migration flows in shaping international integration. In this paper, we investigate the economic determinants of EIA formation using a panel of trade, migration, gravity, and EIA data over the period 1990-2015. We first build a simple spatial model of trade and migration that delivers a structural gravity equation for the movement of people across borders, which we estimate by using changes in EIA membership as changes in migration costs. We find novel evidence on the relationship between EIA participation and migration that suggests deeper agreements generate more migration flows, and these flows exhibit a non-linear relationship with respect to bilateral distance. The model also suggests a set of economic variables that impact the welfare gains from certain types of EIAs, and thus form potential determinants to agreement formation. To this end, we build and estimate a random forest to verify the most important factors that influence the formation of certain levels of EIAs. The random forest suggests the importance of certain country or country-pair economic characteristics in determining EIA membership is heterogeneous across agreement types, and a prediction exercise using distance, contiguity, and country-pair income and income differentials can predict out-of-sample EIA formation with a high degree of accuracy.

This thesis contains three essays that study the effects of government interventions in markets in developing countries. The first essay quantifies the dynamic effects of price control regulations in the Indian cement industry and explores deregulation policies. The second essay studies the effects of large-scale agricultural interventions on farmers and consumers. The final essay examines the role of tariffs and production subsidies in protecting the manufacturing sector. The unifying theme across these essays is the use of industry-specific empirical models to understand the effects of particular policies.

This dissertation examines questions in industrial organization regarding the evaluation of antitrust and procurement policies. In the first two chapters, I examine whether antitrust agencies should consider the effects of product variety changes due to a merger. In the first chapter I test whether the merger had any effect on product variety directly. I find evidence of product variety changes due to a merger in the context of the MillerCoors merger of 2008. I find that the merged firm decreased the number of brands offered and offset this by increasing product variety in more successful brands. However, under a difference-and-differences framework, I find that product variety declined relative to that of other top competitors. In the second chapter, I examine the welfare effects of product variety changes and compare them to the welfare effects of prices to see what should be the priority of antitrust agencies. I estimate demand for the MillerCoors merger in the postmerger period, expanding on previous work in the literature. In a set of two counterfactuals, I test the value of new products created after the merger and the value of discontinued products lost after the merger. I find that the merger increased consumer surplus from changes in product variety. Benchmarking this to results in the literature, I find that the effects of product addition and discontinuation are approximately 34% and -4%, respectively, of the consumer welfare effects of the postmerger price changes in the presence of coordinated pricing found in prior work. In the third chapter, I provide a theoretical framework on how restrictions on firms participating in government procurement auctions affect local government procurement costs, firm participation for procurement decisions and firm investment decisions under the context of the Buy America Policy change in 2015. This policy increased the domestic materials requirement for federal funds to be used for purchases of transit goods, thereby restricting foreign firms from participating in procurement and potentially raising the costs of domestic firms that rely on foreign capital for their products. I find federal transportation funds used for railcars increased while funds used for buses remained flat. I then propose three models to examine the theoretical impact of the policy. The first model estimates a standard first price sealed-bid auction model standard for procurement. The second model separates bidders into two types: one domestic type which draws from a higher mean cost distribution, and one foreign type which draws from a lower mean cost distribution. I find costs and markups increase, while the participation of foreign firms declines.

This dissertation comprises three essays on empirical industrial organization (IO) and applied econometrics. The first and third chapters focus on identification approaches in structural models, with the first chapter dedicated to addressing limitations in demand modeling, while the third chapter studies identification in a triangular two-equation system. The second chapter applies modern econometric tools to understand policy-related topics in IO. The first chapter deals with identification in structural demand modeling, and generalizes the current framework in the literature to achieve a more accurate estimation of differentiated products demand. Within the framework of Berry (1994) and Berry, Levinsohn, and Pakes (1995), the existing empirical industrial organization literature often assumes that market size is observed. However, the presence of an unobservable outside option is a common source of mismeasurement. Measurement errors in market size lead to inconsistent estimates of elasticities, diversion ratios, and counterfactual simulations. I explicitly model the market size, and prove point identification of the market size model along with all demand parameters in a random coefficients logit (BLP) model. No additional data beyond what is needed to estimate standard BLP models is required. Identification comes from the exogenous variation in product characteristics across markets and the nonlinearity of the demand system. I apply the method to a merger simulation in the carbonated soft drinks (CSD) market in the US, and find that assuming a market size larger than the true estimated size would underestimate merger price increases. Understanding consumer demand is not only central to studying market structure and competition but also relevant to the study of public policy such as taxation. In the second chapter, we examine household demand for sugar-sweetened beverages (SSB) in the U.S. Our goal is to understand the distributional effect of soda taxes across demographic groups and market segments (at-home versus away-from-home). Using a novel dataset that includes at-home and away-from-home food purchases, we study who is affected by soda taxes. We nonparametrically estimate a random coefficient nested logit model to exploit the rich heterogeneity in preferences and price elasticities across households, including SNAP participants and non-SNAP-participant poor. By simulating its impacts, we find that soda taxes are less effective away-from-home while more effective at-home, especially by targeting the total sugar intake of the poor, those with high total dietary sugar, and households without children. Our results suggest that ignoring either segment can lead to biased policy implications. In the final chapter, we show that a standard linear triangular two equation system can be point identified, without the use of instruments or any other side information. We find that the only case where the model is not point identified is when a latent variable that causes endogeneity is normally distributed. In this non-identified case, we derive the sharp identified set. We apply our results to Acemoglu and Johnson's (2007) model of life expectancy and GDP, obtaining point identification and comparable estimates to theirs, without using their (or any other) instrument.

This dissertation consists of three independent chapters related to the provision (or underprovision) of urban retail amenities. Chapter 1 (co-authored with Daniel Stackman) asks why storefronts remain empty for more than a year in some of the world's highest-rent retail real estate markets. Landlords with vacancies derive option value from two sources of uncertainty. First, increasing downstream retail demand may drive up market rents tomorrow. Second, different tenants may have different willingness to pay for the same space, creating an incentive for landlords to wait for a particularly high rent offer. We construct a dynamic search and matching model capturing landlords' tradeoff between signing a lease today and the option value of waiting, incorporating key market features including asymmetric commitment to the lease term and high move-in costs. We estimate the parameters by matching quarterly vacancy rates, lease-up rates, and tenant exit rates from a comprehensive, high-frequency storefront tracking service, combined with micro data on commercial leases. In a counterfactual exercise, we find that reducing the variance of the match quality distribution by 50% reduces long-run vacancy rates by 33% on average, while reducing the variance of the aggregate state variable has almost no effect. Finally, we use the estimated model to quantify the impact of a retail vacancy tax on long-run vacancy rates, average rents, and social welfare. Vacancies would have to generate negative externalities of $18.72 per square foot per quarter (about 30\% of average rents) to justify a 1% vacancy tax on assessed property values. Chapter 2 (also co-authored with Daniel Stackman) documents the rise of storefront vacancies in prime retail locations, a phenomenon we refer to as high-rent blight, in Manhattan over the 2016 to 2020 period. We then ask whether this increase in the retail vacancy rate can be attributed to a particular feature of commercial mortgage agreements. In a stylized model, we show that banks impose tenancy covenants (which establish rent floors for new leases landlords sign with tenants) in lending agreements to prevent landlords from impairing the value of collateral in the event of default. These clauses short-circuit the price mechanism in times of falling demand for retail space. We present quasi-experimental estimates suggesting that binding rent floors imposed by mortgage covenants substantially reduce the probability that vacant landlords choose to lease out their space. Finally, we estimate a structural model of landlord borrowing, leasing, and default decisions. In a counterfactual exercise, we show that eliminating covenants between 2016 and 2020 would have reduced the February 2020 vacancy rate by 11.4% (1.5 percentage points) relative to its realized level. Chapter 3 (co-authored with Edward Glaeser and Michael Luca) asks how gentrification transforms neighborhood retail amenities. We present a model in which gentrification harms incumbent residents by increasing rental costs and by eliminating distinctive local stores. While rising rents can be offset with targeted transfers, the destruction of neighborhood character can -- in principle -- reduce overall social surplus. Empirically we find that gentrifying neighborhoods experience faster growth in both the number of retail establishments and business closure rates than their non-gentrifying counterparts. However, we see little evidence that gentrification is associated with changes in retail mix or prices -- suggesting limited welfare losses.

My dissertation examines decision- and policy-making in the innovation and technology ecosystem and during the innovation process. I conduct three studies using novel, highly granular data and rigorous empirical methods. Innovation is inherently risky, and a key question in managing the innovation process is how innovators should allocate resources when information is scarce and environments constantly change. In chapter one, I analyze how large, NIH-supported scientific labs traded off short-term productivity gains (via exploiting safe projects) against information acquisition that could improve long-term productivity (via exploring high-variance projects). I use detailed data on daily resource allocation to over 300,000 research projects from 2000–2015 and estimate a dynamic structural decision model. Results show the labs strongly valued exploration, which had a large and significant positive impact on their productivity. These findings suggest real-world decision-making sophistication and implications for how funding agencies should evaluate innovation productivity. Innovation generates new business models and regulatory challenges. Chapter two explores whether the EU's General Data Protection Regulation (GDPR), a landmark internet privacy law, affected investments in internet infrastructure. I use state-of-the-art internet measurement data and a difference-in-differences approach to examine if the regulation's data-related restrictions slowed investments in the interconnectedness of networks in the European Economic Area (EEA) compared to non-EEA OECD networks. Results indicate zero effects on various measures, suggesting the GDPR's short-run consequences were limited to application firms, its intended targets, rather than networks, its unintended targets. Innovation introduces new products with unclear economic properties and user behavior. Chapter three investigates user decisions around one such product: open-source software. Using data with five million months of open-source web server usage from over 150,000 US organizations between 2000-2018, I develop hazard models to study determinants of organizations' open-source software upgrade decisions. Results show a significant propensity to upgrade through users' routine administrative processes, with counter-intuitive implications for how innovators (developers) should manage releases of new products (upgrades). My future works will continue to study a broad range of managerial and policy issues at the intersection of economics, technology, and innovation management.

In Chapter 1, I describe the hospice industry in California and highlight the key institutional details, then estimate a structural model of hospice choice by patients. Hospices are firms that give palliative care to dying patients. There is no price competition because Medicare pays hospices a fixed per-day rate for each patient, so hospices compete on reputation. I define a hospice's reputation as a stock of its past quality choices. Thus, a hospice can build up its reputation stock over time by consistently choosing high quality. The reputation stock also partially depreciates every period, meaning that a hospice which repeatedly shirks on quality will lose its reputation over time. To study reputation and hospice choice in this setting, I build and estimate a demand model of hospices using yearly hospice-level data from California for 2002-2018. Each consumer makes a discrete choice from a set of hospices in her market, taking into account hospices' reputations and characteristics. The demand estimates show that reputation plays a significant role in consumer choice and depreciates at an annual rate of 53%. In Chapter 2, I build a dynamic oligopoly model of hospices choosing quality to compete on reputation against rivals. This is used to recover the hospice cost function. I use my model and estimates to conduct the following policy counterfactuals. As reputation becomes more persistent - for instance, through the creation of an online hospice rating system - hospices choose higher quality. Hospices also choose higher quality as Medicare prices increase, but the response depends on how differentiated they are in characteristics from rivals. Finally, a hybrid per-day per-visit hospice reimbursement scheme achieves the same quality with nearly 30% lower spending than the current per-day Medicare scheme. In Chapter 3 (joint work with Rena Conti), we study market dynamics in the pharmaceutical industry after loss of market exclusivity by a branded drug. Branded drug manufacturers often respond to generic entry by releasing an Authorized Generic (AG), which is chemically identical to the branded drug but without the brand label attached. This is used to price discriminate between consumers, with the branded drug charging high price and AG charging low price to compete with generics. Using total drug sales and revenue data on US for 2004-2016, we build a stylized structural model to study entry and pricing decisions. We estimate a random-coefficients discrete choice demand model and find significant heterogeneity in brand valuation and price sensitivity among consumers. Then we build a dynamic structural model of generic entry, AG release, and pricing. Combined with calibrated entry-cost parameters, this is used to conduct policy counterfactuals. First, we study the impact of various demand-side policies (such as improving consumer valuation of non-branded drugs and increasing price-sensitivity) on market outcomes. Second, we show that a faster generic approval rate leads to greater generic entry, lower likelihood of AG being released, and lower prices. Third, we find that banning AGs leads to greater generic entry but also higher industry prices overall.

What You Don’t Know Can’t Pass Through: Consumer Beliefs and Pass-through Rates I model consumer search with imperfect information about firms’ costs and test predictions about consumer beliefs and pass-through in the US residential mortgage market. In the model, when consumers are unaware of an increase in costs, a high price would be surprising and may induce additional search. In equilibrium, sellers do not pass though the entire change in costs, and average pass-though is decreasing in consumer uncertainty about costs. The model provides a unified explanation for a number of patterns in pass-through rates including incomplete pass-through (passthrough rates less than one), pass-through over-shifting (pass-through rates greater than one), and asymmetric pass-through (greater pass-through rates for cost increases than decreases). I test a novel prediction of this model using confidential Home Mortgage Disclosure Act data. I find that different components of the marginal cost of mortgage lending have different average pass-through rates. Widely known costs are passed through nearly completely while more obscure costs have much lower pass-through rates. This pattern is not explained by existing models of pass-through, as the standard determinants of pass-through are identical across all cost components of the same mortgage. People Don’t Demand Commitment Devices That Might Not Work Demand for costly commitment devices is rare. A possible explanation is that individuals are unaware of their present bias and their need for commitment. I run an experiment that successfully corrects subjects’ beliefs about their present bias and find that this increased awareness does not increase demand for commitment. These results, interpreted through the lens of a theoretical model of commitment demand, imply that low demand for commitment is not driven by a perceived lack of present bias, but rather subjects’ accurate belief that they may fail to follow through, even with the offered level of commitment. The Illusion of Competition with Michael Grubb Most existing models of price competition in the presence of search costs ignore the possibility that multiple products in a market are sold by the same firm. We develop a theoretical model of equilibrium price dispersion under costly consumer search over prices in the presence of jointly owned “brands.” We establish conditions on consumers’ search technology that determine consumer welfare implications and suggest antitrust remedies (e.g. post-merger consolidation of brands).

This dissertation addresses questions in the health insurance and industrial organization fields. In the first chapter, I investigate how gender-based pricing bans affect health insurance markets offering long-term contracts. In thesecond chapter, I examine lapsing, and its implications, in a health insurance market offering long-term contracts. In the third chapter, I study the long-term implications of product unavailability in the beer market. Chapter 1: In theory, guaranteed renewable (GR) insurance contracts can efficiently insure against reclassification risk without causing adverse selection on pre-existing conditions. In practice, however, adverse selection can still arise on other dimensions. In 2020, in response to protests demanding gender equality, Chile banned gender-based pricing in its private health insurance market. I investigate how this policy impacts Chile’s health care system, which consists of a low-quality public option and a private market characterized by the use of GR contracts. I find that, if the ban is implemented, prices in the private market would increase as low-cost men switch to the public option and high-cost women enter. Overall, the regulation causes a shift of surplus from men to women. The ban is regressive, as high-income groups benefit more than low-income groups, creating a trade-off between gender-based equity and income-based equity. Subsidies that induce low-cost enrollees to remain in the private market are the most effective mitigation strategy to contain higher premiums. Finally, relative to non-GR contracts, the number of individuals choosing the private market is lower under guaranteed renewability. Chapter 2: Guaranteed renewable (GR) insurance contracts have the potential to efficiently protect individuals against reclassification risk without the negative side effects of price regulation, such as adverse selection. For these contracts to work properly, consumers must pay front-loaded premiums when healthy and stick with their contracts for many years in order to subsidize their future high-risk selves. This paper studies lapsing in the Chilean private health insurance markets, a system characterized by the offering of GR contracts. I find that most policyholders lapse their insurance plans just a few years after signing their contracts. I show that policies and lapse patterns predicted by standard theoretical models of long-term contracts are the opposite of those observed empirically. Finally, premiums increasing over time, and consumers lapsing their contracts because of those price changes, are a key determinant of insurers’ profits. Chapter 3: The marketing literature has investigated the processes potentially leading to brand building and the benefits these brands may enjoy over time. One of those possible benefits is resilience in the face of a reputational challenge or a crisis. This chapter focuses on the long-term implications of product unavailability. We leverage a quasi-natural experiment that exogenously removed the top leading beer brands from retail stores for several weeks. We test whether these prolonged stockouts can erode market shares beyond the current or subsequent purchase occasions and study the potential mechanisms at play. Using panel data of consumer purchases before and after the product shortage, we observe that the top brands only partially recovered their pre-stockout market shares, especially among their most frequent buyers. We identify a sizable portion of consumers who tried small brands for the first time during the stockout period and remained to buy those products persistently. To control for prices, state dependence, and product availability, we estimate a choice model with heterogeneous preferences and find that exposure to stockouts has long-run effects on purchase behavior. We interpret our estimates as evidence that consumers facing a restricted choice set may learn or become aware of competing products with long-lasting consequences on preferences.

A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy.

Millions of people do not have access to high-speed Internet in rural and low-income areas. For this purpose, in 2020 the Federal Communication Commission (FCC) launched the Rural Digital Opportunity Fund (RDOF). This was a multi-billion dollar program to subsidize Internet providers willing to offer high-speed Internet in thousands of unserved areas under regulated prices and for the next ten years. To minimize the expenditure in subsidies the RDOF utilized a simultaneous multiple-round auction. This auction presents new empirical challenges to study bidders’ private values and the auction’s optimal design. This dissertation is divided into three essays. In the first essay, I introduce the FCC’s subsidy programs to provide universal access to high-speed Internet, including the rules of the RDOF auction and the dynamic game that the RDOF entails. I then describe the unserved areas, the bidders, and the realized allocation. Lastly, I show that the RDOF was overall successful compared to the previous FCC subsidy mechanism that it replaced. The RDOF procured Internet access in just above 57,000 unserved areas by spending $9.2 billion in subsidies. In contrast, the previous subsidy mechanism, which offered subsidies calculated with an engineering cost model, would have offered $25 billion—almost 200% higher. I conclude by discussing the most closely related literature on empirical methods to estimate private values in simultaneous multiple-round auctions. The second essay introduces an empirical approach to estimate a bidder’s private values—their minimal subsidies to offer high-speed Internet under the FCC rules. Specifically, I model Starlink, a satellite Internet provider that won nearly 34% of the awarded areas. In the model, Starlink requires a lump-sum subsidy per subscriber and a fixed lump- sum subsidy by U.S. state. I derive Starlink’s optimal bidding conditions and use them to estimate Starlink’s private values. I find that Starlink’s private value increases by $300 per subscriber to be served, and that Starlink has a fixed cost by U.S. state bounded from above by five million dollars. I estimate that Starlink’s rent was above 58% of their awarded subsidies and exceeded $500 million in total. The third essay introduces a framework to study incumbent fiber optic providers. In the model, one incumbent has a simple bidding problem and bids for each unserved area up to its private value. In contrast, the incumbent’s rivals are entrant providers, valuing the areas as complements and solving dynamic problems. The econometrician then observes censored values: the incumbent’s values when they are underbid by rivals, and the rivals’ lowest bids otherwise. Rivals’ lowest bids are modeled in reduced form and then jointly estimated with the incumbent’s values by maximum likelihood. I utilize this model to estimate the private values of Windstream in a set of areas that fit in the proposed framework. I find that Windstream’s rent was 44% of their awarded subsidies. I conclude by proposing reservation prices for reducing the government’s expenditure in subsidies.

Firms are key participants in the market. Compared to individual consumers, firms, particularly those with significant market power, are often seen by economists as more resourceful to overcome information frictions, more likely to retain past information and less susceptible to bounded rationality. As a result, firms are capable of acting as sophisticated strategic players. Under the assumption of being driven by a clearly defined profit motive, firms may behave strategically when interacting with other market participants, including consumers, other firms and governments. Understanding firms’ strategic behaviors is a critical step in assessing market efficiency, analyzing the wellbeing of consumers, and designing or evaluating government policies. This dissertation consists of three essays that analyze firms’ strategic behaviors in different settings. Chapter 1 studies firms' strategic interactions in government organized spectrum auctions. In these ascending-bid auctions, firms as bidders are able to communicate their private information with one another using jump bidding as signals. The signals are credible since bidders with lower private information incur a higher ex ante cost for choosing a jump bid with any given size. This prevents the bidders with lower private information from mimicking those with higher private information. In equilibrium, the signaling model predicts lower expected revenue to the seller, in this case the government, than in the "open exit" model in which jump bidding is not allowed. Using data from a spectrum auction held by the Federal Communications Commission in the United States, the mean valuation estimated using the signaling model is higher compared to that of the open exit model. This implies that if bidders are indeed using jump bids as signals, ignoring it leads to estimates of the mean values that are biased downwards. This result is consistent with the prediction of the theoretical model that bidders pay lower prices with jump bidding than in an open exit auction. I estimate that if jump bidding was prohibited, the government could have had 8% higher revenues from the auction. In Chapter 2, my coauthors and I evaluate a government policy that subsidizes the agricultural equipment rental markets in India. We observe that private rental firms favor farmers located in dense areas and demanding higher machine-hours because equipment needs to be moved in space. Using our own census of 40,000 farmers, we document that costly delays and price dispersion in rentals are ubiquitous, and that small-scale farmers are rationed out by private rental firms. This rationing could be detrimental to aggregate productivity if small farmers have the highest marginal return to capital. A government subsidized first-come-first-served dispatch system grants small-scale farmers timely access to equipment at the expense of travel time. In a calibrated model of frictional rental services, optimal queueing and service dispatch we show that, while the constrained efficient allocation prioritizes large-holder farmers, small-scale farmers in dense areas are valuable because they help maximize capacity utilization. Through counterfactuals, we show that when the induced increase in subsidized equipment supply is high enough, service finding rates for small-farmers increase relative to large-holders farmers even when providers prioritize large-scale. In Chapter 3, I offer an alternative explanation to the existing theories on why firms make the strategic decision to carry out planned obsolescence. Planned obsolescence refers to the practice of firms choosing durability levels for their products below the cost-efficient ones. Motivated by the Phoebus cartel, whose reason for engaging in planned obsolescence cannot be explained by existing theories, I introduce a new theory that centers on an important concept from behavioral economics: present-biased preferences. I construct a theoretical model which demonstrates that when consumers are present biased, that is, when they exhibit time-inconsistent preference in favor of immediate gratification, a monopolist chooses a profit-maximizing level of durability below that chosen by a perfectly competitive market with the same production technology.

This dissertation is comprised of three essays, each dealing with topics in empirical Industrial Organization and Applied Microeconomics. The second chapter was co-authored with Amil Petrin and Boyoung Seo and the third chapter was co-authored with Veronica Postal.\\ \noindent In the first chapter, I develop a dynamic model of the oil pipeline industry to estimate the impact of direct price regulation on investment. Since the shale boom began in 2010, crude oil production in the United States has surged over 100\% leading to a dramatic increase in demand for pipeline transportation. However, the profitability of investing in oil pipelines is constrained as transportation rates are set subject to a price cap. In this chapter, I examine the impact of direct price regulation on pipeline investment in response to the shale boom. I develop a theoretical model of the pipeline industry, where firms make production and investment decisions while being subject to a dynamically changing price ceiling. I estimate the model using detailed operational data derived from regulatory filings and compare welfare under three separate regulatory environments: price cap regulation, cost-of-service regulation, and price deregulation. I find that price cap regulation was superior to the alternative mechanisms considered, as it increased market entry by 15\% and incentivized firms to operate 17\% more efficiently. I find evidence suggesting that prices were allowed to increase too quickly. While this led to an increased rate of entry into new markets it came at the expense of higher prices in existing markets. This ultimately resulted in a transfer in consumer surplus from existing customers to new customers and a slight decrease in total relative to what could have been achieved under a fixed price ceiling. \\ \noindent In the second chapter, we propose a novel approach to estimating supply and demand in a discrete choice setting. The standard Berry, Levinsohn, and Pakes (1995) (BLP) approach to estimation of demand and supply parameters assumes that the product characteristic unobserved to the researcher but observed by consumers and producers is conditionally mean independent of all characteristics observed by the researcher. We extend this framework to allow all product characteristics to be endogenous, so the unobserved characteristic can be correlated with the other observed characteristics. We derive moment conditions based on the assumption that firms - when choosing product characteristics - are maximizing expected profits given their beliefs at that time about preferences, costs, and competitors' actions with respect to the product characteristics they choose. Following \cite{Hansen1982}, we assume that the ``mistake'' in the choice of the amount of the characteristic that is revealed once all products are on the market is conditionally mean independent of anything the firm knows when it chooses its product characteristics. We develop an approximation to the optimal instruments and we also show how to use the standard BLP instruments. Using the original BLP automobile data we find all parameters to be of the correct sign and to be much more precisely estimated. Our estimates imply observed and unobserved product characteristics are highly positively correlated, biasing demand elasticities upward significantly, as our average estimated price elasticities double in absolute value and average markups fall by 50\%. \\ \noindent In the third chapter, we estimate the benefit households derived from the introduction of light rail transit in Minneapolis. The primary goal of this chapter is to decompose this benefit into two components: the direct effect from improved access to public transportation and the indirect-effect from the endogenous change in local amenities. The literature has predominantly relied on two methods to estimate the impact of public transportation: difference-in-differences models and hedonic pricing models. Difference-in-difference models yield convincing treatment effect estimates but do not readily provide a decomposition of the direct and indirect effect. Hedonic pricing models can provide such a decomposition but have historically relied on parsimonious specifications that do not control for omitted variable bias. Recently, researchers have proposed refining the hedonic pricing approach by incorporating predictive modeling, where the researcher trains a predictive model on a control group using a high-dimensional dataset and then uses this model to predict what prices would have been in the ``but-for" world for the treatment group. The difference between actual and predicted prices provides a valid estimate of the average treatment effect. However, if important sources of heterogeneity are excluded from the model then this approach will still suffer from omitted variable bias. We propose augmenting the estimation of the predictive model with instrumental variables allowing us to control for the selection bias induced by unobserved heterogeneity. We find close agreement between our predictive model and the difference-in-differences approach, estimating an increase in house prices of 10.4-11.3\%. Using the predictive model, we estimate that prices increased by 5.5\% due to improved access to public transportation and 5.8\% due to improved access to amenities. \\

Electric vehicles (EVs) have peaked people’s interest since the first electric-powered vehicle was developed in the 1830’s. By the 1890’s EVs were the most common vehicle in the U.S. However, due to issues with charging batteries and limitations of the electrical grid, EVs were left behind as gasoline vehicles became commonly available in the early 20th century. Several attempts to repopularize EVs over the last 100 years suffered from difficulties with limited driving ranges, but today, expanded driving ranges and improved charging capabilities have made EVs a real alternative to gasoline engines. The possibility of zero-emission transportation made possible by EVs has fueled both public and private investment in EV charging infrastructure through a variety of subsidies. However, a better understanding of how charging stations are utilized and of the effects of charging station subsidies on drivers and station development is essential. This dissertation studies how charging station subsidies, station location, and prices affect driver charging behavior and new station construction.

I explore questions related to financial institutions, the accessibility of financial services, community development, and the political economy of forgiving student loans using the ideas from spatial economics, industrial organization, and public choice. Increased access to financial services improves the economic outcomes of individuals and firms in the community. Understanding sources leading to a lower accessibility of financial services is integral for an effective endeavor to lower barriers to financial services with far-reaching policy and economic implications. The economic literature provides evidence of the beneficial effects of increased access to financial services as well as the adverse effects of diminished access to credit and banking services. These effects are particularly pronounced for low-income individuals, minorities, and small businesses. The research also shows that, despite the technological advances, credit and depository service markets are local. Hence considering the geographic aspects of these services is essential. My research explores the role of geography in bank exit and entry, which directly affects the spatial accessibility of financial services within a community. Similarly, a rapid increase in student-loan debt has drawn much attention form public, scholars, and politicians. The sharp increase of defaults on student loans accompanied by growing student loan debt during the Great Recession led to proposals of forgiving student loans and making some public higher education tuition-free. To understand the political economy of such proposals, I explore the circumstances that motivate the implementation of the student-loan debt forgiveness policy in a two-period model of schooling and unemployment insurance with search costs.

My dissertation focuses on issues related to the technical efficiency of energy firms as well as the integration of U.S. natural gas markets during the era of shale gas expansion of the early 2000s. In the first chapter, I use nonparametric methods to estimate changes in efficiency and productivity of natural gas pipelines in the U.S. during the period of shale gas expansion during 2007--2018. This period, known as the Shale Revolution, saw an increase in shale gas production from less that 5 billion cubic feet per day (BCF/d) in 2007 to over 60 BCF/d in 2018. This increase in production coincides with increases in capacity and infrastructure improvements to the transportation network of natural gas pipelines. Previous research in energy economics has not examined changes in efficiency and productivity of natural gas pipelines during the Shale Revolution using modern nonparametric techniques. To guide my estimation procedure I use new nonparametric tests to make inference on the properties of the production set of pipelines. These nonparametric tests indicate that the production set for pipelines changes with production year, is convex, and does not exhibit constant returns to scale. Moreover, I find strong evidence of increases in the technical efficiency and productivity of pipelines during the period of shale gas expansion from 2007 through 2018. In the second chapter, I use nonparametric methods to estimate changes in productivity and efficiency of major electric utilities in the U.S. during 2001--2019. Starting in 2007, an increased supply of natural gas resulted in large-scale adoption of natural gas fueled energy production as well as an overall increase in installed generating capacity. Data on major electric utilities, collected by the Federal Energy Regulatory Commission (FERC), indicate that the overall installed generating capacity of utilities increased by 13 percent during 2001--2013, with an increasing share of capacity coming from plants fueled by natural gas. However, this increase in capacity coincides with a decrease in the growth of utility-scale electricity consumption and a 12 percent decrease in utility energy generation. The evidence suggests a decline in the productivity of energy generation from electric utilities during the sample period. In addition, using modern nonparametric techniques reveals a decrease in mean technical efficiency of utilities during the period of decline in energy generation. Finally, other modern nonparametric tests show that the production frontier of electric utilities changes depending on the production year and share of natural gas fueled generating capacity a utility has out of their total capacity. This indicates that nonparametric efficiency estimates should be conditional on time and the share of natural gas fueled generating capacity. In the third chapter, I use unit root tests to measure the degree to which geographically dispersed natural gas markets in the U.S. became integrated into the same market from 1996 through 2019. Previous papers examining natural gas market integration in earlier periods suggest that some regional natural gas markets in the U.S. became integrated into the same market during the early 1990s. This was likely the result of regulatory reform instituted by FERC in the late 1980s and early 1990s. Nonetheless, previous studies also indicate that some regions remained distinct markets, particularly the eastern and western U.S. Unit root tests of the price-gaps between geographically dispersed price hubs fail to reject that the eastern and western gas markets were distinct markets prior to the early 2000s. After 2001 a higher proportion of the east and west price-gaps became stationary, suggesting that more price hubs in each region responded to similar market shocks. This provides some evidence that the eastern and western U.S. natural gas markets may have been integrated into the same market starting in the early 2000s. Moreover, the qualitative results of this paper hold under four different unit root tests.

This paper proposes a new methodological approach to define rail markets by usinggeospatial clustering to assign terminals into disjoint local markets. These new railmarkets then resemble the unidirectional city-pair from the airline literature. This newdefinition of railroad markets is applied to estimate the determinants of fuel surcharges,especially market shares, as implemented under the current regulatory safe harborregime. Motivational analyses show shipments where a fuel surcharge is implementedhave 6% higher rates than shipments where a fuel surcharge is not implemented. It is thenestimated a 10 percentage point increase in a railroad’s market share is associated with anincrease of 5% in the fuel surcharge.

This dissertation consists of three chapters covering topics in empirical industrial organization. The first chapter considers how consumers respond to reductions in the net weight of packaged goods. The second chapter documents the use of pricing zones among US grocery retailers. Finally, the third chapter analyzes the market power of Visa credit cards. The first chapter examines how consumers respond to product downsizing where firms increase unit prices by reducing the content of their packaging. I consider whether consumers are inattentive to reductions in package content since they tend to underuse statements of net weight. I build a structural model of consumer preferences that incorporates inattention to changes in net weight and apply it to grocery store scanner data. I apply the model to a downsizing event in the black pepper industry where the industry’s largest firm shrunk five of its products. I show that approximately half of all consumers fail to notice the reductions. With full information, consumers would switch to larger packages that provide greater welfare. In the second chapter, Joseph Simmons and I document the extent of zone pricing among top grocery retailers. Using data from grocery and home improvement retailers, we develop a machine learning algorithm that identifies pricing zones from point of sale data. We apply our method to pricing data from some of the top grocery retailers. We find that these large grocery retailers price their products using a small number of zones. Moreover, the number of zones does not change over a nine-year period, despite significant improvements in information technology during this time. This suggests that an inability to distinguish demand across stores is the primary impediment to greater price discrimination. The third chapter evaluates the market power of Visa credit cards by exploiting an event where the grocery retailer Kroger stopped accepting Visa credit cards at two of its grocery chains. I employ an event study methodology to examine how foot traffic changes at the beginning and end of the ban. I find that foot traffic decreases by six percent at the start of the ban, implying that Visa has substantial market power. Because Kroger offered price discounts and other promotions during this time, my result may represent a lower bound on the true effect. However, the results are sensitive to violations in the identifying assumptions.

In many markets, efficiency depends on the quality of information that participants have. However, participants may face frictions in accessing information, which could result in significant welfare losses. My dissertation studies the causes and consequences of information frictions, focusing on the security analyst market and the public cloud market. The first two chapters investigate how information frictions are generated in the security analyst market. Security analysts observe signals and compete to make forecasts on securities’ earnings, which serve as public information to investors. Here I study how analysts’ incentives affect the quality of information they provide. In Chapter 1, I consider security analysts’ incentives as a whole and estimate them using revealed preference. Security analysts are rewarded for being more accurate than their peers. This reward for relative accuracy leads analysts to distort their forecasts to differentiate themselves, but also disciplines them from being overoptimistic. I structurally estimate a contest model with incomplete information to capture both effects, disentangling the payoffs for relative accuracy, optimism and absolute accuracy. Using the model, I conduct counterfactuals to evaluate policies that reduce analysts’ payoff for relative accuracy. I simulate the effect of these policies on the quality of information in terms of forecast errors and variances. The reward for relative accuracy reduces errors by 33 - 58%, but increases variances by 4%. It is optimal to have moderate competition between the covering analysts of each security. In Chapter 2, I ask where these incentives come from. Are analysts motivated by dynamic incentives of reputation, or by short-term compensation such as bonuses? I show with reduced form evidence that low-reputation analysts may face more incentive to outperform their rivals than high reputation analysts. Building on this, I develop and estimate a dynamic model where analysts compete to build reputation and earn compensation. I find that analysts face a strong reputation-building incentive because high reputation is associated with a much higher fixed wage. Meanwhile, their forecasts have an insignificant impact on their immediate compensation. Chapter 3 studies the consequences of information frictions in the public cloud market. Firms need information about available technologies to make good adoption decisions. Inattentiveness to such information may create stickiness to outdated technology. In a joint project with Sida Peng and Peichun Wang, we study the welfare benefits of firms’ public cloud adoption and the consequence of consumer inertia in this market. We develop a novel demand model that allows for both multiple product choices and continuous quantities on each product. We estimate the model using a proprietary dataset on individual firms’ cloud usage history from a major public cloud provider. The estimated average return on investment in cloud is 2.2 times the cost of investment, which is driven by smaller firms disproportionately benefiting from access to computing resources on the cloud. On the other hand, inertia on the cloud leads to sub-optimal product choices for all firms and reduces welfare from cloud usage by almost 62%. We show that introductory discounts incentivizing firms to try new products can improve both consumer welfare and provider revenue.

Essay 1. Since the GFC, banking regulators are increasing regulations to lower the risk of banking crises occurring in the future. These have focused on liquidity, asset composition, and capital requirements. This paper focuses on the loan rate effects of raising bank capital requirements. Previous calculations of loan rate effects predominately use the WACC formula, which implicitly assumes perfect competition. Banking is an industry where firms have market power. This paper develops a tractable model of an imperfectly competitive banking market, where the implicit cost of capital is naturally included in the optimization problem of the bank. I find that the magnitude of loan rate changes depends on the market structure and provide estimates for a calibrated Australian market. %This provides some validation of studies that use a blended MM approach. Essay 2. Using a tractable two-loan type banking model I analyse recent changes in capital regulation in Australia and NZ. My modelling shows, IRB banks were significantly advantaged by Basel II in generating ROE, but that subsequent changes in capital settings have slowly moved towards competitive neutrality. In addition, I analyse the minimum average risk weight policy for IRB banks (2016) and find that IRB banks are motivated to hold a higher proportion of risky loans and undoing composition efficiency. Moving back to risk-sensitive risk weights is desirable, but using a scalar multiple or correlation adjustment, reinstates the large advantage IRB banks have in low-risk loans. Lastly, I analyse RBNZ's proposed large increase in capital requirements using a two-country banking model. Due to NZ's dependence on IRB Australian banks, I find the loan rate impact of these changes depends on APRA's approach, with the loan rate effect much smaller than documented by the RBNZ. Essay 3. In Australia, mortgage brokers (MB) are paid by banks an upfront commission and a trail commission, rewarding the length of time a borrower stays with a bank. Both Hayne (2019) and PC (2018) recommend banning the trail commission but differ regarding who should pay the upfront commission, banks or customers. This paper uses a 2-period IO model of a mortgage market to analyse different MB remuneration options. I find if aggregator/MB firms are efficient, borrowers will benefit from banks' paying an upfront commission, with aggregator/MB firms no worse off and bank profits lower. In contrast, customers paying an upfront commission can be better for borrowers but will be worse for both aggregator/MB and banks. If a proportion of borrowers balk at paying MBs the commission, borrower gains are diminished, aggregator/MB firms are worse off, and some bank profits recouped.

The U.S. coal mining industry was once a booming industry which created and defined communities, particularly in Appalachia. The industry has, however, transformed significantly in the last couple of decades with the passage of environmental policies, with competition from the Shale Revolution, from changes in company ownership, and from mine safety regulation. Overall, the coal industry during this time has experienced a massive decline in production and employment. This dissertation is composed of three papers that investigate these mechanisms and their role in understanding market structure, coal transactions and prices, and mine safety outcomes. Motivated by the shutdowns of U.S. coal mines, Chapter 1 uses data from the Energy Information Administration (EIA) and hand-compiled coal parent company data, to conduct short- and long-run coal mine survival analyses using mine-plant transaction data. It hypothesizes that utilities that faced higher delivery coal prices were more likely to replace coal-fired with natural gas powered generators. Conditional on the overall U.S. natural gas to coal price trend as well as proxies for mine and plant exposure to environmental policies, the two-stage least squares results indicate that the mean difference in coal delivery price in 2010 between Appalachian mines and other mines causes the mine-plant survival rate to differ by 12.1 percentage points over the 2010-2020 period. Chapter 1 concludes that the mass exits of coal mines in Appalachia arose primarily because underground mines typical of Appalachia are more expensive to operate than surface mines typical of other regions. Following Chapter 1, Chapter 2 looks more closely at how coal markets for electricity generation are concentrated and localized within the United States, particularly Appalachia. Using mine-power plant transaction-level data over the 2009-2019 period from the U.S. Energy Information Administration (EIA) and hand-compiled data on operating companies’ parent companies and individual owners, Chapter 2 shows that an average power plant sources coal from 4 parents and that around 30% of power plants source coal from a single coal parent. It finds that, conditional on the overall trend, local concentration in Appalachia increased coal delivery prices, with power plants paying up to 9.6% more for coal when sourcing exclusively from a monopolist parent company. Using the same hand-compiled research as Chapters 1 & 2, Chapter 3 investigates coal mine safety and the behavior of coal mine safety violators. This Chapter covers 2002-2018, also using data from the Mine Safety and Health Administration (MSHA) and the United States Energy Information Administration (EIA). It was inspired by the passage of the federal Mine Improvement and New Emergency Response (MINER) Act of 2006, which aimed to improve mine safety in the United States after the deadly Sago mine disaster. It finds that while some underground mines were reported to be less negligent after the Act, safety did not improve uniformly across all mines. Finally, Chapter 3 finds that financially constrained parent companies regardless of listing status are responsible for exposing their miners to more potentially fatal working conditions.

Abstract: Recent advances in genomics have revolutionized selective breeding in many agriculturally important animal species such as dairy cattle, poultry, and pork. However, the adoption of genomic selection in the beef industry has been slower for reasons such as the existence of multiple breeds, the poor extent of phenotyping, lack of use of artificial insemination, and lower profit margins. Sharing genomic data between Breed Associations has been considered a solution and some information sharing platforms have emerged. Information sharing can also support the declining beef demand by improving meat quality. However, information sharing also makes BAs compete on product consistency, correlates their strategies, and increases signal variability. Our aim in this thesis is to analyze the viability of IS to individual BAs in the seedstock sector. We use the study of oligopolistic competition under uncertainty to develop a game-theoretic model. Two models are proposed. In the basic specification, we consider the cases of full information sharing versus no information sharing. In the next specification, we allow for coalitional information sharing where breed associations can share in a coalition. In the basic model, we found that in general, information sharing is less likely to occur when information is valuable to the breed association - either for improving the quality of the trait or the production decision. Second, we found that BAs with a large market size will not share information in a market with close substitutes. Finally, we found that information sharing increases profitability in expectation but makes the profit less predictable. One implication of this result is that Full Information Sharing may happen eventually as BAs have a better prior. In the extension of the model, we found that coalitional information sharing will prevail for most values of market differentiation, except when BAs sell very strong or weak substitutes. This implies that once coalitions are established, it is hard to achieve full information sharing. Another implication is that the presence of IS coalitions introduces beef product differentiation in terms of quality.

This dissertation addresses open questions in economics surrounding the industrial organization and regulation of recreational cannabis markets. The first chapter empirically studies how the impact of location restrictions on strategic cannabis retailers affects market outcomes and resident welfare. The second chapter provides a different method to measure the externality that cannabis retailers impose on heterogeneous residents and how it varies with different land-use policies. The third chapter investigates the presence of consumer demand response and inertia in vertical relationships among buyers and sellers in a new market. The first chapter studies the welfare impact of land-use regulations, such as location restrictions for businesses, which generally involves a trade-off between market growth and the harm on affected parties and is ultimately an empirical question. I study how location restrictions near sensitive-use areas affect recreational cannabis retailers' decisions in Washington State and their effects on residents, consumers, and taxation. Using property sales data, I first show reduced-form evidence that cannabis retailers act as a disamenity when close to homes and to their assigned schools. Then, I develop a structural model of consumer demand and firm entry and location and estimate it on a comprehensive dataset of retail sales and inventory transfers. The model incorporates the various market regulations as constraints on the firms’ strategic decisions. I use the estimated model to conduct counterfactual land-use policies. Relaxing buffers around sensitive-use areas, like schools, from 1000 ft down to 100 ft benefits consumers and harms residents but, on net, results in welfare and tax revenue improvements. The second chapter is a more in-depth study of the other side of the coin, resident disamenities of cannabis retailers, where I develop a discrete-choice, static model of housing demand and supply, and estimate it with property sales and mortgage applicants data from King County, Washington. By allowing for heterogeneous households, I can incorporate the role of cannabis retailers as a potential (dis)amenity to households. I then compute the welfare impact of various alternative location configurations for retailers that arise from the counterfactual land use policies studied in \cite{Larsen2020a}. I find that resident surplus decreases on average but that the valuation for cannabis retailers is heterogeneous, with residents with children being the main demographic negatively affected. Lastly, the third chapter studies the supply chain of the market to understand the factors that make-or-break vertical relationships and how a market matures. The third paper uses a rich dataset of inventory transfers for the Washington State recreational cannabis industry to assemble a novel dataset of vertical relationships between producers and retailers starting from the market’s inception. In the early stages of the market, I find substantial structural state dependence within vertical relationships, suggesting firms are reluctant to sever their existing business links due to poorly performing products and serves as a cautionary tale against making assumptions about firms when not in equilibrium. As the market reaches an equilibrium, product performance becomes the leading factor for producer-retailer matching. A theoretical model of consumer demand and link formation is provided to rationalize these findings.

This dissertation consists of three essays in the areas of Industrial Organizationand Applied Microeconomics, examining the role of social interactions on market outcomes and the welfare consequences. The first essay studies the role of social influence on consumer demand and firm competition through pricing strategies. Social influence is an important driver of consumption behavior, but its effect on firm competition and pricing is understudied. This paper investigates whether and how social influence affects product choices and firm competition, drawing on a novel dataset that consists of large-scale de-identified mobile call records from a city in China. I first identify social influence using a new identification strategy that exploits the partially overlapping network of friends and residential neighbors and the intertemporal variation in friend circles. I find that the purchasing probability for a phone model doubles with 10 percent more friends using the same model. Consumers are more likely to conform to wealthier friends and choose visually distinct features, suggesting that status-seeking motivation may be an important driver of social influence. I then evaluate how social influence affects firm competition by building and estimating a structural model that incorporates social influence in consumer demand. I find that social influence favors high-quality products while reducing low-quality products’ market share. In addition, a small price drop of a product would lead to larger gains through quantity expansion by peers. Social influence, on average, reduces initial prices by 0.7 percent and increases subsequent prices by 0.1 percent. It also increases the total profits of new products by 3.4 percent and increases consumer surplus by about 1.7 percent. In the second essay, my co-authors and I examine the role of social referrals and information exchange in urban labor markets. We use the universe of deidentified and geocoded cellphone records for over a million individuals from a major Chinese telecommunication provider. We find that information flows, as measured by call volume, correlates strongly with worker flows, a pattern that persists at different levels of geographic aggregation. Conditional on information flow, socioeconomic diversity of the social contacts, especially that associated with the working population, helps to predict the worker flows. We supplement the phone records with administrative data on firm attributes and auxiliary data on job postings and residential housing prices. Referred jobs are associated with higher monetary gains, a higher likelihood to transition from part-time to full-time, reduced commuting time, and a higher probability of entering desirable jobs. The third essay studies the effects of parental retirement on adult children’s labor supply through intergenerational time and monetary transfer. My coauthor and I exploit the mandatory retirement age in China as the cut-off point and apply a regression discontinuity (RD) approach to four waves of the China Family Panel Studies (CFPS) Dataset. Our findings suggest that parental retirement reduces adult children’s annual hours of labor supply by 3 to 4 percent. This reduction is especially pronounced for female children. We find that the reduction can be explained by parents’ increasing demand for time and care from children due to the significant drop in parents’ self-rated health upon retirement. Although both male and female children increased their monetary and time transfers to parents, we find that parents tend to make more transfers to sons compared to daughters. Daughters are also more likely to make transfers to parents after they retire, both in terms of money and in terms of time.

This dissertation consists of two chapters on the industrial organization of ocean shipping. In the first chapter, I study the investment decisions of US ports. Transportation infrastructure is characterized by two opposing forces: economies of scale that encourage centralization, and spread-out consumers that encourage dispersion. These forces may not be correctly balanced in the United States as decisions are made by many different regional authorities which receive large federal subsidies. I study seaports during a period when those on the East Coast were making investments to prepare for the larger vessels that could navigate an expanded Panama Canal. With data on all container imports and capital costs of major US ports, I estimate a model of the investment game that port authorities play. Competing ports invest more than a social planner would, even allowing for deviations from profit maximization, because they do not internalize their business stealing effects on others. In particular, the $1.7 billion expansion of the Port of New York and New Jersey would not have been chosen by a coastal authority. Social surplus would be over a billion dollars higher with coordination, the equivalent of about one year's worth of revenue for all the East Coast ports. Lowering federal subsidies appears to lower much of the gap. In the second chapter, I show how port productivity changed after a historic labor agreement. Across many industries, employers and workers often argue over technology adoption. In 2008, the International Longshore and Warehouse Union signed a contract agreeing that all ports on the US West Coast could fully automate their terminals, recognizing there would be job losses. Using a new, ship-level dataset of the labor it takes to unload ships, I study changes in productivity after the contract was signed and after one port automated. Having the ship-level data is important, as I show more aggregate measures would produce misleading estimates. I find productivity increased about 25% as a result of the new contract and an additional 15% among the port that actually automated. I find that the effects did not completely persist through the 2015 contract, even though the automation clause did not change, and suggest possible ways employee-employer relations may alter outside the written contract.

Why do so many firms successfully bring innovative new products to market, only to fail shortly thereafter? I argue that early sales are critical in improving a product's characteristics, leading to more sales: the result is a ``bottle rocket" economy where a minor difference in initial trajectory leads a firm to accelerate to the heavens or just as rapidly crash to earth.Chapter 1 concerns the role of reliability growth in dynamics. A firm can observe problems encountered by early adopters and modify their blueprint accordingly. Trial-and-error thus allows successful products to become more reliable, and thus even more successful, posing a challenging ``competitive moat" to entrants. I apply this model to the market for space launches, showing that observable reliability growth explains the longevity of rockets from the 1950s, the expensive failure of the majority of new orbital launch vehicles, and the apparent backfiring of US sanctions meant to stem the growth of the Chinese space sector.Chapter 2 extends this model of reliability growth to software: crucially, automatic updates allow early adopters to benefit from continued product improvement. This results in a dynamic game of imperfect information: consumers adopt a product based on both current value and expected future improvements, while firms must decide if delivering on expectations is profitable. I find that firms release patches at a relatively steady rate until support is ended completely, and present an equilibrium where protracted steady patching serves as both a direct product improvement and a credible signal for long-term support.Chapter 3 concerns social influence in adopting a new product: users can delay adoption until network effects strengthen or social consensus is positive. I establish a micro-foundation for the canonical ``social influence" model of adoption, and develop a novel nonparametric estimator the heterogeneity of user preferences underlying an observed adoption curve. Using adoption curves from the market for video games, I show that this algorithm produces estimates consistent with known product characteristics, i.e. the relative importance of social features versus independent early adopters.

This dissertation encompasses three works on nuclear power plants (NPPs). A theme common to all chapters is the question of why nuclear power has failed to achieve the success envisioned by its proponents, particularly when that success has been achieved at certain times and in certain places yet failed to continue into the present day or disseminate globally. I address my research questions with a theoretical framework informed by the study of political economy, which I argue is necessary to understand this politically-charged subject. In Chapter 1, I study lead time---the duration of construction and commissioning---which is an important determinant of the capital cost of NPPs. For an industry dominated by a handful of multinational firms, the degree of cross-national variation is surprising. NPP lead times have historically trended upwards over time in Western nations, and yet they are comparatively quick and stable in East Asia. I theorize that the institutional capacity and autonomy of subnational governments can partially explain these patterns in the data. Having assembled a novel dataset on the design specifications of the global population of NPPs, I empirically document a positive association between political decentralization and NPP lead time that is not explained by observed cross-country differences in NPP design. The results are suggestive of the hypothesis that political decentralization creates conditions that slow NPP construction for non-technical reasons. However, the findings are not robust to certain robustness checks and fail to rule out the possibility that unobserved differences in design explain this association.In Chapter 2, I study the operational reliability of NPPs, which has globally trended upwards since the 1970s. Previously, Davis and Wolfram (2012) showed that the transfer of NPP ownership from vertically-integrated utilities under cost-of-service regulation to independent power producers operating in competitive wholesale electricity markets substantively contributed to the upward trend in reliability in the United States. However, international data reveal persistent and large cross-country differences in NPP reliability. Notably, NPPs in the United States substantially outperform their peers in other highly developed economies, even those with earlier and more comprehensive liberalizations of their electricity sector. The present work extends the analysis of Davis and Wolfram (2012) to nearly the global population of NPPs and encompasses a more diverse set of ownership structures and regulatory frameworks under which countries restructured their electricity markets beginning in the 1990's. I find the effects of restructuring on NPP reliability vary widely by country, with the clearest successes in the United States and Canada, but muted or even negative impacts elsewhere. Chapter 3 focuses specifically on the United States and makes three contributions. First, I present novel empirical evidence to support prior qualitative and historical work which characterizes the regulatory environment for NPPs in the United States as having dramatically escalated, beginning circa 1970. Second, I find a positive partial association between certain regulatory phenomena and the time required for a reactor to receive a license and begin commercial operation, subject to several controls. Among other results, I show that state intervention in reactor licensing (which is formally solely under federal purview) is positively associated with longer licensing duration, specifically in the 1970s but not earlier. Third, I ask whether the licensing hold-up achieved its stated goal of increased reactor safety. While my methods lack causal identification, I show that reactors which took longer to receive an operating license exhibit a noticeably lower rate of common, low-level safety incidents in comparison to other reactors of the same age and technical characteristics. Finally, in Appendix A, I present a novel dataset of NPPs that encompasses technical, economic, political, regulatory, and geographical characteristics of the global population of commercial nuclear power plants. I combined a large number of publicly available datasets with extensive original data collection and cleaning. My database is the backbone of this dissertation and should be of use for many possible future research projects. Except for certain restricted-access data provided by the International Atomic Energy Agency, I have made the data available for public dissemination at https://github.com/a-g-benson/Global-NPP-Database.

Drawing on theory from development economics and industrial organization, I study the economics of rural firms and markets to understand how different types of shocks affect firm operations. I examine three types of shocks: technology, weather, and prices. I use a combination of randomized experimental variation and quasi-experimental variation to identify the effect of each type of shock on small firm outcomes. The primary outcomes are relational contracting with suppliers and customers, firm performance (sales, profits, hiring of workers), number of competitors, and changes to input and output prices.In the first essay, I evaluate how a technology shock designed to lower search costs affects how firms interact with their suppliers and customers. For small firms, search frictions interfere with learning about new suppliers in their upstream market, and raise the cost of meeting new customers in their downstream market. Using a randomized experiment of 507 small firms, I study the impact of a digital phonebook that lowers the cost of accessing new business and customer contacts. Participating firms are split into a control and treatment group with two variations: 1) a phonebook listing that is visible to upstream suppliers in urban areas, and 2) a phonebook listing that is visible to downstream customers in rural areas. I find that treated firms increase relational contracting with their suppliers and decrease it with their customers. Yet, there is no strong evidence that the number of new customers or suppliers increases. This pattern suggests that being listed in the phonebook caused firms to update their valuation of relational contracts and respond by negotiating better terms with suppliers and customers.In the second essay, I study how a weather shock that lowers agricultural production affects rural firms whose customer base experiences crop losses. In the absence of insurance and credit markets, the effect of adverse weather shocks on rural firms is ambiguous because drought shifts both demand and supply curves. I use spatial and temporal variation in the 2016-2017 drought in Kenya to characterize the direct and indirect effects of drought-induced food insecurity on local firm outcomes. Firms in areas directly affected by drought have lower sales, profits, and hire fewer workers than firms in non-drought areas. Firm entry also increases in drought areas compared to non-drought areas, consistent with prior evidence that farming households form new businesses as a coping strategy following shocks. Subsector analysis reveals substantial heterogeneity. Service firms fare better than retail firms. But examining retail sub-sectors shows that firms selling higher-value food products (meat/fish and fruits/vegetables) experience greater declines than staple grain sellers in markets directly affected by drought, while firms selling high-value foods increase sales in non-drought areas. This is consistent with consumers in drought regions decreasing consumption of non-necessities.In the third essay, I explore how input price shocks passthrough to consumer prices for rural and urban firms. Price variation is a typical feature of markets in developing countries. Rural firms face substantial price variation when purchasing goods (inputs) that they re-sell as outputs. How much of this input variation passes through to prices for rural customers? Rural households rely on local businesses to purchase household food staples and other essential commodities. Yet, relatively little economic literature examines retail passthrough rates of these essential food staples to clarify how it affects local food security. I use a panel of input and output prices for 230 urban firms and 240 rural firms to evaluate passthrough from idiosyncratic input price shocks on key commodities sold through urban-to-rural supply chains. I find that rural firms smooth both negative and positive input price shocks more than urban firms. By exploring possible mechanisms, I find suggestive evidence that smaller community size is associated with lower output prices, suggesting that social ties may play a role. At the same time, competitive pressure matters as well - rural firms who face new entrants and have higher absolute number of competitors have higher passthrough rates, consistent with a competitive market framework.

The first chapter is a joint paper with my committee member as well as my mentor, Dr. Myongjin Kim and Dr. Kerry Tan Loyola from University Maryland. We study the impact of tacit collusion on price dispersion in the U.S. airline industry. We find that tacit collusion driven by multimarket contact has a positive effect on prices, but a negative effect on price dispersion. Our empirical results suggest that airfares throughout the price distribution increases, yet the price distribution becomes more compressed since 10th percentile airfares increase by a larger amount than 90th percentile airfares. Moreover, we also find that this pricing phenomenon does not exist if Southwest Airlines is present on the route. Thus, route-level price competition is softened when the same airlines directly compete more frequently, except when Southwest Airlines services that route. As such, our empirical analysis provides evidence that the presence of Southwest Airlines exhibits an anti-collusive effect. Chapter 2 is a joint work with my mentor, Dr. Myongjin Kim and Dr. Qi Ge from Vassar College. This paper examines US airline mergers between 1993 and 2018 and studies their impact on the labor market. Our difference-in-differences estimates indicate a significant reduction in the merging airlines' long-term wage and fringe benefits following the mergers. The effect is particularly salient among large-scale mergers involving major airlines and low cost carriers. The results also suggest a negative short-term employment impact of mergers that varies by occupation types. Our findings are consistent with the impact of merger-induced monopsony power discussed in recent literature and offer important policy implications regarding how to account for employer monopsony power during mergers and acquisitions. In the third chapter which is a joint work with my best friends, Brent Norwood and Sean O'Connor, we identify the cross and inter-state effects of marijuana legalization on house prices using a national housing data set from the online real estate listing database Zillow.com. We find positive effects upwards of ten percent in the top half of the price distribution following successful legalization ballot initiatives, and between five and fifteen percent across the distribution after the state enacts the ballot initiative and the first legal sales take place. A spatial difference-in-differences model reveals that within Colorado and Washington, prices in neighborhoods with new dispensary openings nearby experience a seven percent price appreciation. To summarize, our results suggest that there are second order benefits to marijuana legalization.

We replicate the reduced-form analyses of Evans and Kessides (1994) and Ciliberto and Williams (2014) to find empirical evidence of multimarket contact increasing price levels in a market. We then apply the fixed-effects model to price dispersion but find inconclusive results. We follow in the footsteps of Ciliberto and Williams to use gate ownership at an airport as an instrumental variable. We find evidence of the power of the instruments through first-stage regressions and strength tests. The application of the instrumental variables provides interesting but inconclusive results for both price levels and price dispersion.

This dissertation uses economic theory, in tandem with experiments and other empirical methods, to better understand the role of information – for instance, missing information, conflicting information, and information overload – in determining outcomes in decision settings characterized by risk and uncertainty. In my first chapter, I use theory and experiments to compare market outcomes in a setting where the seller has better information on product quality than the buyer, and examine the effects of introducing a third-party who can credibly relay information on product quality. Under a range of conditions, I find market efficiency is higher when the information intermediaries interact with buyers instead of sellers. Moreover, market outcomes depend on many other factors, including the accuracy of the certification process and the level of competition in the certification market. My other dissertation chapters examine decision making in settings characterized by ambiguity, where agents may not form unique beliefs about the probabilities associated with potential outcomes either because there is too little information, or too much conflicting information is provided. In the second chapter, I design a novel experiment that takes advantage of information updating to test the predictive power of two popular classes of ambiguity aversion models, and the experimental results suggest that the “kinked” rather than “smooth” specification better predict subjects’ behavior. In the third chapter, I conduct MATLAB simulations to study the timing of technology adoption in response to climate change when predictions from multiple climate models are available. Using the data from California’s Central Valley, my simulation results show that an increase in the confliction between projections accelerates the adaption speed of new technology, while an increase in the volatility of projections delay the procedure.

My three essays on behavioral economics and mechanism design introduce two new microeconomic theoretical models. In the first chapter, we develop an n-player theoretical model applying the concept of Virtual Bargaining to study cooperative behavior in public goods games characterizing team production. Virtual Bargaining is a modeling framework that characterizes how players may construct a tacit agreement to coordinate behavior in the absence of explicit communication. Players identify their worst-possible payoff outcome from any candidate agreement, and mutually best-respond with respect to maximization of their worst-payoff function. Players face uncertainties regarding whether other players will follow through on a candidate agreement or play their Nash best response to candidate agreement. The worst payoff function is the minimum over these possibilities. We show that, relative to the Nash equilibrium predictions, the virtual bargaining model predicts more effective coordination with higher contributions to the public good when individual contributions are strategically complementary and the public good production technology exhibits decreasing return to scale. This type of public good characterizes team production processes when each player receives a share of the total team output, and effort by each player increases the marginal product of every other player. In the second chapter, based on the different theoretical predictions of the VB and standard Nash models, we propose an experimental design to test whether the VB or the standard Nash model predicts people’s behavior better. What's more, we also test the VB theory in a price-setting duopoly market environment. In the third chapter, we show that hidden information regarding the quality of placements of online advertisements by publishers can lead to inefficiency in the market that is a form of moral hazard. We then characterize an incentive-compatible contract between the publisher and a traditional contract advertiser in the online display advertisements environment. This solves the inefficient impression allocation problem between the traditional contract market and the real-time bidding spot market. Unlike previous papers, which rely on including the reputation or long-term benefit consideration into the publisher’s objective function, our incentive-compatible contract characterizes a one-time profit maximization problem for the publisher.

This dissertation is comprised of three essays. The first one studies the valuation of novel safety technologies such as airbags and anti-lock brake systems by consumers using real-world data on car purchases. The second essay looks at the interaction between government regulation and adoption of airbags by car manufacturers, as well as the welfare implications for the market. Finally, the third chapter utilizes the data on car sales to study the evolution of markups and concentration in the US automobile industry throughout the last 24 years. In the first chapter I study the economic value of the new safety technologies that were introduced in automobiles in the 1990's. While these features reduce the risk of death and injury in traffic accidents they are often expensive and not all consumers are willing to pay for them. I find that an average consumer values driver airbags at the level of $861, anti-lock brake system - at $1,043, and side airbags - at $1,633. Overall, the introduction of frontal airbags improved consumer welfare by an equivalent of about $1,345, however it affected the consumers differently based on their characteristics. Understanding the value that consumers place on these technologies is helpful in guiding the policy related to regulation of car safety standards. The second chapter studies the effects of technology-forcing regulation that was announced in 1991 and required all new cars to be equipped with airbags starting in 1998. This government intervention forced firms to adopt new technology earlier that they found optimal, creating a market distortion. I develop and estimate a structural model of consumer vehicle choice and dynamic airbag adoption decision of the firms in the presence of a deadline. I construct a detailed data set of car sales for more than 2,600 car models and 16 distinct car features over 13 years. The results show that government regulation was a major driver of airbag adoption. The mandate forced 100% of cars to have dual airbags by 1998; however, in the counterfactual, without the airbag mandate, only 60% of new cars would have dual airbags in 2002. I also find that the regulation had heterogeneous effect on consumers. When comparing the world without the airbag mandate to the world with the airbag mandate, consumers experienced an average welfare gain of 1.7% because airbags were available earlier and cheaper. However, the airbag mandate reduced welfare for consumers with high price-sensitivity and low valuation of airbags by 5.2%. Firms that adopted airbags before the regulation requirement experience 1.3% lower profits compared to the world without the mandate because the mandate limited their ability to differentiate themselves from the competition. In the third and final chapter I investigate the development of markups and firm concentration in the automobile market from 1990 to 2014. I estimate markups for all the firms active in the market in these years using a structural model of consumer car choice. The findings show that the markups have slightly decreased by during the period covered in my dataset. This confirms the findings in the literature for the markups in the automobile industry.

Companies are implementing new strategies to meet the customer requirements in terms of quality, timing, and cost. One of these strategies is cross-docking, which can be defined as the process of consolidating the products coming from different suppliers, but having the same destination, with minimal handling and almost no storage between loading and unloading of the goods. The purpose of this research is to investigate the benefits of having a cross-docking facility in a supply chain. In this research, we focus on developing discrete event simulation models using the opensource Java Simulation Library (JSL). Also, we work on augmenting an object-oriented library for simulating supply chains to include the modeling of cross-dock facilities. The modeling of a crossdock facility includes the receiving, staging/sorting, and load building activities. Because the operational performance of the inner workings of the cross-dock is not needed, detailed modeling of the resources within the cross-dock such as the number of workers, and pieces of equipment are not included in the modeling. However, the flow, time delays, and inventory aspects are modeled because the key emphasis is on how the cross-dock affects the performance of the supply chain. Simulation experiments are conducted to test the performance of the object-oriented library and to compare the performance of two multi-echelon inventory networks with and without crossdocking to identify the significant factors which affect the performance of the two types of supply chains.

The three essays of this dissertation consider rich network-like environments. In these environments, properly modeling the intricate relationships between agents is the key to capturing the important economic mechanisms that take place. In the first essay I introduces a flexible Bayesian structural model suitable for the analysis of social learning, competition, and diffusion in environments where firms consider entry to a new market. The model is estimated exploiting a reform in Illinois that legalized slot machines, and empirically study how information and adoption diffuse through a network. I find that when small firms make adoption decisions they react to others’ outcomes, yet they do not use all the relevant information. Moreover, they learn from more firms than they compete with. Finally, increasing information availability or learning substantially increases both adoption and total profits in the market. The second essay is co-authored with Krishna Dasaratha and Ben Golub. The essay is a theoretical study of an environment in which agents that are embedded in a social network learn about a changing state of the world. We examine when agents can aggregate information well, responding quickly to recent changes. A key condition for good aggregation is that each individual's neighbors have sufficiently different types of private signal qualities. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses. The third essay studies biases that experts could suffer from when they provide their opinions. This paper suggests a method on how to put values on many alternatives, in a case when there is a small number of bilateral comparisons. This method is then used to create a ranking of all Division-I college football teams that is based solely on wins and losses. Comparing this ranking to the official experts-based ranking, I show that the expert opinion ranking is biased towards teams with higher viewership and more star players. At the same time, another experts’ ranking that is not used by the NCAA is not biased towards teams with high TV viewership, suggesting that the bias of the experts may be due to some incentives they have, either explicit or implicit.

This dissertation contains three essays aimed toward understanding the causes and consequences of firms' demand for debt finance. The first chapter studies how the currency mix of multinational firms' debt responds to relative changes in country-level interest rates. According to the principle of interest rate parity, firms should not respond at all. Relative movements in interest rates should be accompanied by movements in exchange rates that exactly offset the benefits of borrowing in low-rate currencies. However, I find that firms rebalance their debt away from currencies where interest rates rise, and toward currencies where interest rates fall. I examine five reasons why firms might behave this way, and find evidence against three reasons and in support of two. I also find that firms adjust the currency structure of their debt when they have better access to foreign capital markets and more financial flexibility. Finally, declines in foreign policy rates boost investment for firms with the best foreign-market access, but the incremental investment may not occur in the country where rates fall.The second and third chapters show that patterns in local firms' demand for bank finance influence the behavior of individual banks and the structure of the banking industry. Many studies find that bank industrial organization helps structure nonfinancial industries via the credit supply, but these chapters argue that causality also runs in the other direction.The second chapter examines changes in demand for bank finance around the replacement of locally-owned establishments by large national corporations that borrow in bond markets and from money-center institutions rather than from small local banks. This chapter specifically studies the effects of Walmart's entry into a market. I show that Walmart's entry decision is strongly influenced by potential sites' distance from Walmart headquarters in Bentonville, Arkansas, and I take this geographical characteristic as otherwise unrelated to bank behavior. The new Walmart store displaces local competing establishments but does not replace their demand for local bank finance. In the five years after a Walmart store opens, nearby banks expand real estate lending, reduce business and agricultural lending, and become more likely acquisition targets. Surviving banks also grow larger. At the county level, the number of bank branches per population also declines.The third chapter models a profit-maximizing bank in an adverse selection setting where soft information is important and where banks develop industry-specific expertise. In this model, the shape of the distribution of borrowers across industry is of first-order importance. The model predicts that markets where borrowers are concentrated into just a few industries have higher small-bank market share, higher loan rates, lower probability of bank exit through acquisition or failure, and more concentrated local banking markets. Empirical results are consistent with the model's predictions.

My dissertation studies three cases in which the perception of current policy or beliefs about future policy shaped economic decisions. Issues of attention and beliefs about the future can have tremendous importance both because agent actions reveal what they believe about the future, such as in the first and third chapters, or because misperception change how agents are affected by policy, such as in the second chapter. The first chapter measures the effect of the 2017 Tax Cuts and Jobs Act on share prices of publicly traded firms, finding that the most profitable firms, and those in concentrated industries, benefited the most. The tax bill significantly reduced corporate tax rates, thereby increasing share prices, particularly at the top. Among firms with the highest profit rates, more than 80 percent rose in value on news of the tax reform, whereas among less profitable firms, fewer than 60 percent appreciated. By every measure, stock market gains coincident with the tax bill were concentrated among firms with greater market power. This pattern is consistent with economic rents being important components of the values of large U.S. corporations. The second chapter of my dissertation looks at how to measure welfare in a world where tax policy might be misperceived by agents. Recent developments in behavioral public economics have shown that heterogeneous biases prevent point identification of deadweight loss. The second chapter replicates this result for an arbitrary (closed) consumption set, whereas previous results on heterogeneous attention focused on binary choice. It finds that one can bound the efficiency costs of taxation based on aggregate features of demand. When individuals have linear demand functions, the bounds for deadweight loss are easy to calculate from linear regressions. While the first chapter of my dissertation looks at the effects of expected policy, the third and last chapter looks at higher-order moments. Forecasts of the consequences of tax changes usually assume that economic actors expect these changes are permanent, despite the inevitable political uncertainty that could lead to future reversals or further changes. This reasoning extends to when a firm's tax burden is correlated to the success of its ventures. The third chapter shows how a firm's belief about how government policy is correlated with the input's marginal product distorts its risk profile, leading it to change its input decision. Generally speaking, input use will be discouraged if the firm faces high taxes precisely when the input is more productive. The last chapter shows that in a world of policy uncertainty this holds under an arbitrary tax system, and in particular it holds even if inputs can be perfectly deducted from the firm's taxable income. Whenever the covariance between policy and payoff is zero, the model replicates the classical result that the deductibility of input expenses leaves the decision undistorted. The third chapter uses this theoretical relationship in an empirical model of asset pricing to infer what investors believe about how future government policy correlates with their risky investments in different firms in the stock market. Each chapter’s analysis can be read on its own, but the unifying theme is that in each case issues of perception and belief were central in either identifying current beliefs about the world, or in understanding the real impact of economic policy on agent welfare.

This dissertation presents several empirical and methodological results in industrial organization, with a focus on settings with microdata or lightly aggregated data. Chapter 1 estimates a model of search and price discrimination in the US home mortgage market, using microdata from two types of consumers to identify the model. Consumers who fail to recall the interest rate pay more for their mortgages, with most of the disparity explained by price discrimination. Chapter 2 estimates an equilibrium model of the US auto insurance market. Consumers face search and switching costs, which firms take into account in their pricing decisions. Counterintuitively, consumers may be harmed in aggregate by lower search costs. Chapter 3 considers the standard problem of estimating logit or mixed logit demand, but in disaggregate data where markets are too small for the market shares to reliably equal the choice probabilities. I adapt binomial regression to estimate a multinomial logit model and show that a version of the Salanié and Wolak (2019) linearization can be applied to binomial regression to approximate the mixed logit model.

This dissertation studies competition in the pharmaceutical and airline industries, focusing on two types of firm conduct: advertising and pricing. In the following chapters, I apply econometric models to novel datasets to study important policy questions in these industries. The first chapter studies the effects of direct-to-consumer advertising (DTC) on the market shares of new branded prescription drugs. This chapter contributes to the debate in the literature over whether advertising is more advantageous to entrants, who might use it to gain traction in the market, or to incumbents, who might use it to persuade consumers away from the entrants. Moreover, the permissibility of DTC for prescription drugs is a contentious policy issue. I focus on Type 2 diabetes drugs because this category has seen a wave of brand entry in the last decade. To account for the endoegeneity of advertising, I use political advertising as an instrument. I find that DTC has a business-stealing effect on the margin. In order to assess the effects of a counterfactual policy which bans DTC, I estimate a discrete-choice model of drug demand by diabetes patients. I find that new brands would have lost significant market share under a DTC ban, and that patients would have substituted to cheaper drugs. These results suggest that the desirability of DTC hinges on whether the therapeutic benefits of new drugs outweigh their higher prices. The second chapter extends the study of DTC to an additional element of firm strategy: pricing. High drug prices are a cause for great concern in the United States, particularly for diabetes drugs such as insulin. The effect of advertising on prices is theoretically ambiguous, however. This chapter studies the question empirically in the market for diabetes drugs. I find that the marginal effect of DTC is to lower prescription drug prices. This is explained by business-stealing – DTC makes consumers more willing to substitute between drugs, which limits drug manufacturers’ ability to set high prices. To study the effects of a ban on DTC, I augment the demand model of the first chapter with a model of drug price negotiations between insurers and drug manufacturers. I find that both higher and lower average prices are possible after a ban. I also find that the results of the first chapter are robust to allowing for price changes – market shares of new brands fall in both cases. These results show that price adjustments should be taken into account in the policy debate around DTC. The third chapter, with Paul Brehm and Andrew Usher, examines the relationship between airfares and oil prices in the Australian airline industry. We find that pass-through exceeds 100% on average and increases with competition. We also find evidence of heterogeneity in pass-through across different products – business class pass-through is lower than economy, while non-stop flights have higher pass-through rates than flights with stops. These results reflect a tension between second-degree price discrimination and pricing higher to high-value consumers. Heterogeneity in consumers’ willingness to pay and the existence of products targeted at different consumers are key to explaining these results. Our findings have important implications for environmental policy in industries with imperfect competition and differentiated products. In particular, they suggest that a carbon tax on the airline industry would be shifted onto consumers to a great extent.

My research analyzes the impact that new technology can have on consumers' welfare and the strategic responses from firms. My dissertation focuses on the television industry, which has experienced dramatic changes within the last decade with improvements in technology and the introduction of streaming video services. The entry of streaming video services is important to study because this new product has had a disruptive effect on the economic environment for the television industry, including direct and indirect effects on consumer welfare. In chapter one, I explore the implications of the direct effect of streaming video services on consumer product choice and consumer welfare. To quantify the benefits from streaming video services, I build a model for the demand of television services in the United States. The model uses the multiple-discrete choice framework, which allows for consumers to choose a traditional package, a streaming video service, or a combination of these services. To estimate the model, I construct a new market-level dataset with prices, product characteristics, and observed demand for 2014 and 2015. I then conduct three counterfactual simulations to measure the change in consumer welfare when a product is removed from the choice set. The simulation results suggest that removing the streaming service has around three times as large of an effect on consumer welfare as compared to removing a satellite provider. Then, in chapter two I explore the indirect effects that the entry of streaming services had on consumer welfare through cable television package prices. In particular, I examine the strategic response to this new product for the cable package prices. I quantify this effect by expanding upon the methodology from chapter one to include a supply-side Bertrand-Nash equilibrium with differentiated products. Then, I use GMM to estimate the cost parameters for cable providers. After these parameters are estimated, I run a counterfactual simulation that removes the streaming service from the consumer's choice sets and re-evaluates the equilibrium prices for cable providers. I find that the entry of streaming services may have led to a $15 increase in cable prices per month, and the median consumer welfare effect is $6.26 per month, which means that as of 2014 and 2015 the median household would be willing to pay $6.26 per month to avoid the higher cable prices from the entry of Netflix. Finally, changes in the economic environment from the entry of a new product will have implications for competition. Therefore, streaming services should be considered when analyzing antitrust cases, new policies, and regulations. In the third chapter, I use the models and data from chapters one and two to examine a proposed horizontal merger between the top two cable providers in the era of streaming services to understand the impact that this merger would have on consumer welfare. The counterfactual simulation in this chapter quantifies the consumer welfare effect from the proposed merger between Comcast and Time Warner Cable. I find that the median consumer welfare effect is $4.01 per month, which means that as of 2014 and 2015 the median household would be willing to pay $4.01 per month to avoid the higher cable prices from the merger of Comcast and Time Warner Cable. Therefore, the antitrust authorities were justifiably concerned about this proposed merger.

Maverick firms are defined in the Horizontal Merger Guidelines as those firms that may exert a disproportional competitive effect in markets where they compete. The Guidelines mandate that mergers and acquisitions involving maverick firms be given special consideration by the Agencies, however not much is known about maverick firms or their competitive effects when they are acquired. The Guidelines describe characteristics that may be present in a maverick firm, but stop short of providing a discrete test that may be used for their identification. They are often small firms whose acquisitions do not warrant reporting to the Agencies due to falling under the thresholds in the Hart Scott Rodino Act, and when they are reported, they are often difficult to identify. With the increase in acquisitions of startups and other disruptive firms by dominant incumbents as seen in the tech sector, there is public policy interest in assessing the possible damages or benefits that may arise in mergers and acquisitions of maverick firms. This dissertation takes a look at two mergers in two different industries that may shed light on these questions. The first chapter analyzes a merger in the airline industry. Using publicly available data from the Department of Transportation, I use a difference-in-differences and a triple difference approach to analyze the price effects of the merger between Southwest Airlines and AirTran Airways, two maverick firms in the airline industry. I find anticompetitive merger-related price effects that are multiple times those of previous merger analyses in the airline industry of non-maverick firms. The results suggest that the merger weakened the firm’s own incentives to act as a maverick due to the elimination of maverick competition, as well as its incentives to act as a maverick towards other firms. The second chapter analyzes the acquisition of a maverick firm by a non-maverick dominant incumbent in the beer industry. I use the Nielsen Scanner database and a difference-in-differences approach to uncover the merger-related effects on price, quantity, and variety of offerings due to the acquisition of Goose Island by AnheuserBusch Inbev. A fear among policymakers is that dominant incumbents will acquire mavericks to release competitive pressure on themselves or foreclose parts of the relevant market to other startups. I find the contrary to be true in this acquisition, with the incumbent firm maintaining price competition while expanding sales and encouraging entry into the market for craft beer.

This dissertation explores the role that organizations play in bringing scientific innovations to society. Chapter 1 situates this work in the current landscape of innovation research and motivates the need for further research on this topic. Chapter 2 explores the role thatfailure, both technological and regulatory, plays in understanding how organizations make future investments in innovative projects. I find that following FDA rejection, biopharmaceutical firms become significantly less likely to further invest in unrelated products already under development. However, they experience a higher proportion of future successes, as they redirect investment into less risky innovations. In contrast, I find no evidence of these effects in response to technological failures at the end of clinical trials, suggesting that this effect is not driven by the loss of firm value nor does it support a traditional Bayesian updating framework. Rather, these findings are consistent with the idea that there is a difference between failure at the technological level versus failure at the decision making level.Chapter 3 illustrates how the boundaries of an organization influence the type of innovations in which organizations do and not choose to invest following a sudden reshuffling of consumer demand. I demonstrate that a sudden increase in market size (and therefore expected revenue) increases an established firm’s propensity to make larger investments in products in their pipeline that are less likely to receive approval. However, I find that this result only holds for those organizations that diversify into fewer therapeutic spaces and are additionally more centralized. I theorize that, in line with findings from organizational economics and internal capital allocation inefficiency, this is due to management having greater control over resource allocation decisions in more centralized firms.Finally, Chapter 4 studies how the type of innovation pursued may affect market outcomes and competitive interactions between organizations. Using drug repurposing as a research context, I explore how the repurposing of a pharmaceutical drug for a new disease impacts its sales, and the sales of its competitors, for other approved uses. By leveraging variation in the combination of diseases that one drug treats and the timing of those disease approvals, I find a positive spillover effect of repurposing on sales of the drug for other diseases and this effect also spills over into the drug’s close competitors. Furthermore, I find that this growth in sales comes at the expense of competitors further away in therapeutic type. These findings have important implications for a pharmaceutical firm’s R&D strategy and the strategic responses to be made by competitors.

Finance is a significant part of any individual's life. The accessibility and quality of finance is an outcome of the financial intermediating infrastructure, and largely influences household welfare. This dissertation aims to understand how the detailed architecture of financial intermediation affects household finance in their real estate investment. Mortgage finance plays a crucial role in the US economy and there is significant government intervention. What are the consequences of different types of government intervention in the US housing market is a central question, especially after the Great Recession. While much effort has been devoted to understanding government guarantee programs which aim to facilitate the secondary market of mortgage loans, the first chapter "Government-Sponsored Wholesale Funding and the Industrial Organization of Bank Lending" evaluates a less-studied government-sponsored wholesale funding program in support of the primary mortgage lending market--Federal Home Loan Banks (FHLB). I exploit quasi-natural variation in access to low-cost wholesale funding from the FHLB arising from bank mergers, and show that access to this funding source is associated with an 18-basis-point reduction in a bank's mortgage rates and a 16.3% increase in mortgage lending. This effect is 25% stronger for small community banks. At the market level, a census tract experiences an increase in local competition after a local bank joins the FHLB, with the market concentration index (HHI) falling by 1.5 percentage points. This intensified local competition pushes other lenders to lower their mortgage rates by 7.4 basis points, and overall market lending grows by 5%. Estimates of a structural model of the US mortgage market imply that the FHLB increases annual mortgage lending in the US by $50 billion, and saves borrowers $4.7 billion in interest payments every year, mainly through changing the competitive landscape of the mortgage market.Another feature of the US mortgage market is its well-developed secondary market, full of various structured finance products. Among these structured finance products, credit derivatives such as credit default swaps (CDS) are largely viewed as redundant securities or side bets that do not have any influence on the underlying mortgage lending activities and household access to mortgage credit. Contrary to this view, the second chapter "Match-Fixing in the Mortgage Finance Field: Credit Default Swaps and Moral Hazard" explores the ex post moral hazard problems in the subprime mortgage backed security market, and empirically demonstrates that the existence of CDS protection alters the incentives of market participants, which affects the performance of the underlying mortgages. Specifically, CDS sellers encourage borrowers in their mortgage pool to refinance, in order to unload the CDS sellers' obligation to cover the loss of the underlying mortgages. Consequently, the mortgages in CDS referencing pools are 3.6% more likely to be refinanced and 2.1% less likely to default. To mitigate the endogeneity concern that CDS sellers choose to write CDSs on better mortgages, this paper further explores the local randomization due to the discontinuous sale of mortgages by their originators, to establish a causal relationship between CDS coverage and mortgage refinance and default performance. This paper provides the first direct evidence that credit derivatives can affect fundamental assets through the ex-post actions of derivative traders. The third chapter "Residential Investment and the Business Cycle" focuses on the connection between household investment in housing and the macro economy. To achieve a smooth path of consumption, consumption surplus can be stored either in the form of manufacturing capital through business investment, or in the form of housing structures through residential investment. The return of business investment is exposed to the corporate bankruptcy risk, since potential bankruptcy of the holding firms would incur huge displacement cost to the specialized assets. The residential investment, however, is free from such risk due to the generic function of housing service. Therefore, the investors would shift more resource from the residential side to the business side in response to the low bankruptcy rate in economic booms. This mechanism could explain why the share of residential investment in GDP decreases before the advent of the crisis while the business investment acts in the opposite way.

There is a potential of digitalization in improving sustainability throughout the entire building process. One way to address this potential is through BIM-LCA integration. Life-Cycle Assessment (LCA) is a method for analyzing the environmental impact of buildings. Building Information Modelling (BIM) is a methodology that can help to account for LCA during the Building Process. Current industry practice and research show that there are mainly two approaches for BIM-LCA integration. Either the evaluation is done in a simplified way at the beginning of the building process, or it is accounted for at the end when the needed information is available, but it is too late for having an impact on decision-making. There is, therefore, a lack of methods and tools for implementing BIM-LCA integration over the entire building process. Based on this, the objectives of this study are to analyze the potential of integration of LCA methodology in BIM practice in the Architecture, Engineering, Construction, and Operations (AECO) industry and to define a methodology for BIM-LCA integration in the Swiss context. For the achievement of the objectives, a parametric tool for BIM-LCA evaluation is created and tested on a case study. The case study is provided by the Swiss construction company Implenia AG. Through the application of the parametric tool on the case study, it is shown that the entire building process (SIA 112, 2001) can be evaluated for its environmental impact continuously in each Building Phase (SIA 112, 2001) while applying an existing code structure (crb, 2012). In that way, the re-work that characterizes most of the current LCA practices is minimized and decision-making metrics, both at Element (SIA, 2018) and Building (MINERGIE, 2014) Levels, are provided.

These essays explore sources of inefficiency and mismeasurement, as well as possible routes for improvement via policy, in China’s coal power market and manufacturing sector. In the first chapter I develop a new model to quantify the behavior of market planners in China’s coal power sector as well as how this behavior affects the investment decisions of power plants. Chapter 2 compares different approaches to measuring and diagnosing the possible sources of inefficiency in China’s coal power market. Chapter 3 takes a broader look at the manufacturing sector in China to examine how differential treatment of state-owned enterprises should be accounted for in estimates of aggregate productivity for China.

This dissertation addresses open issues in complementary goods mergers and tacit collusion as it relates to the freight railroad industry. It provides a broad overview of the freight railroad industry, its leading players, the rate-setting process, and describes dynamics in the markets of different commodities shipped. The standard literature on tacit collusion concentrates on how it influences the pricing of substitutes. However, collusion is also likely to influence the pricing of complements. For example, in static equilibrium, if two local monopolists were selling complementary products, they would charge a higher price than if both products were offered by a single multiproduct monopolist, reducing both the industry profits and the consumer surplus. However, if firms were able to coordinate, they could reach a Pareto improvement by lowering prices to the monopolist level. Therefore, in the markets where firms sell both substitutes and perfect complements, the welfare effect of coordination is ambiguous. The dissertation analyses this question in the context of the US freight railroad industry. Using rail waybill data, I find evidence that prices are higher on average in markets where the route is served jointly by two or more railroads, and thus inefficiency from the pricing of complements is present. I then estimate a structural model where firms set prices a la Bertrand and conduct merger simulations for the firms that sell complements in many markets. I find that mergers are welfare enhancing. They benefit the consumer and merging parties but hurt outsiders. In the last chapter, I estimate a structural model of competition with conduct parameter defined as a function of multimarket contact. I compare industry welfare to the counterfactual of breaking tacit collusion and full monopoly and find that the former is welfare enhancing. The latter reduces the welfare but the effect if smaller in magnitude.

This dissertation consists of three chapters on healthcare markets. The first chapter presents the question of whether skilled nursing facilities are selectively admitting patients and provides preliminary evidence of those practices. The second chapter presents a structural model of selective admissions and simulates counterfactual policies using model estimates. Together, these first two chapters show the prevalence and impact of selective admissions in the nursing home industry, as well the effect of potential government policies intended to mitigate selective admission practices. The third chapter assesses the effects of a hypothetical US reference pricing policy that would cap prices in US markets using those offered in other countries, such as Canada. This third chapter is joint work with Pierre Dubois and Shoshana Vasserman.

This dissertation analyzes the men's razor market to examine whether a monopolist can implement price discrimination for the complementary goods. I estimate a demand system for razors using the random coefficient logit model with market level sales data from the Nielsen Store Scanner dataset and individual demographic data from the March CPS. The estimated parameters are used to construct price-cost markups. By comparing the markups of different products, I find evidence that Gillette uses a two-part tariff strategy. This conclusion can be generalized as that of a monopolist setting the prices of tie-in products consistent with a two-part tariff.

This dissertation consists of three essays examining how and why firms set prices in markets. In particular, this dissertation shows how firms may utilize nonlinear pricing to price discriminate, how firms may experiment with the prices they set to learn about the demand function in the market they serve in later periods and the effects of these pricing strategies on consumer welfare. In Essay 1, I show how firms in the milk market use nonlinear price schedules -- quantity discounts -- to price discriminate and increase profits. I find that firms have a greater ability to price discriminate on their own ``private label'' products rather than regional branded that they sell alongside their own. Though some consumers benefit from a lower price as a result of the price discrimination, total consumer surplus is lower than if the store had to offer a fixed price per unit. Additionally, I compare my structural demand estimates, which using the Nielsen household panel data include consumer demographic information and actual household choices, to the standard approach in the literature on price discrimination that uses only market level data. By doing so I find that ignoring demographic information and actual consumer choices leads to biased parameter estimates. In the case of the milk market, the biased parameter estimates due to ignoring household demographic information and actual consumer choices lead to underestimating welfare harm to consumers on average. After finding that price discrimination harms consumers overall in this market, I quantify which consumer demographic are better off and which are worse off. I find that households with children and low income households with children are the only households to benefit from the price discriminatory practices of firms in this market. Since these groups are particularly vulnerable, I suggest that policymakers take no action to correct this market, as any action will directly hurt these consumer groups. In Essay 2, I study how firms learn about the demand in a new market by exploiting a significant change in Washington's state's liquor laws. In 2012, the state of Washington switched from a price-controlled state-store system of selling liquor to one in which private sellers could sell liquor with minimal restrictions on price and range of products. As a result, a heterogeneous group of firms entered the liquor market across the state with little knowledge of the regional demand for alcohol in the state of Washington across heterogeneous localities. Using the Nielsen retail scanner data I am able to observe the variation in pricing and offerings seasonally and over time to see if there is convergence in offerings and prices, and how quickly that convergence occurs across different localities depending on local demographics and competition. I also investigate the extent to which the variation is "experimentation'' by the firms, i.e., the firms purposely experimenting to learn more about demand and the extent that local demographics and competition can affect the experimentation and whether there are spill-overs from local competition (i.e. do firms learn from each other and does this effect how much they experiment and how quickly they learn). My main findings are that over time, firms within this market have learned better how to price discriminate over the holiday season; firms experiment more with prices for the pint sized products than the larger sizes; and that menu of options that firms have offered has been expanding but at a slower rate, suggesting that they are approaching a long-run steady state for the optimal menu of options.

The objective of this dissertation is to analyze the economic impact of newly operational ethanol biorefineries on rural counties in the U.S. Midwest region for the period 2001 to 2015 using a quasi-experimental approach. Rapid growth in the ethanol industry expanded the number of ethanol plants located in the U.S. Midwest from 54 in 2001 to 173 in 2015. Out of the counties with 119 new ethanol biorefineries, 97 counties met the general treatment criteria defined in this dissertation, but only 56 of those counties qualified for the rural treatment criteria. Counties with ethanol biorefineries that qualified for treatment were organized into a treated group based on county level data. Six counterfactual control groups (or control counties without ethanol biorefineries) were contemporaneously matched to the treated counties based on the Mahalanobis distance metric evaluated on a set of 29 selection variables. Matching occurred on two levels. In the first level, matching was performed both for the in-state level and over the entire Midwest region. In the second level, three criteria were used to select the final control groups: Mahalanobis distance metric best match, population best match, and rural-urban continuum codes (RUCC) best match. Economic impact is evaluated based on the growth rate in real per capita earnings for the treated group over a period from one to five years after treatment relative to the control group. A difference-in-differences (DID) model is used to assess the significance of results where the dependent variable is the natural log of real per capita earnings and a set of control variables is used to capture state fixed effects, time fixed effects and spillover effects. Empirical results evaluated against a representative Midwest control group and over six regression models adjusting for various fixed effects produced, on average, one-sided significant results for average treatment on the treated (ATOT) with a (min, max) range of growth rates as (5.53%-7.63%), (10.0%-12.0%), (14.7%-19.6%), (14.5%-18.3%), and (13.3%-18.9%) from one to five years after treatment, respectively. The minimum value of these estimates can be represented as an uncorrected average annual growth rate as 2.75%, 3.33%, 3.68%, 2.90%, and 2.22% over the respective period from one to five years after treatment. Employment levels for the treated group increased on average by 211 at the county level five years after treatment. A comparative Midwest control group lost, on average, 169 jobs over the five year period after treatment. A treated county employment multiplier calculated using the direct, indirect and induced employment impacts varied from 1.46 during the year of treatment to 7.6 five years after treatment relative to the control group. Five years after treatment, the treated group employment rate gradually increased, on average, by 2.2% which was better than either of the two counterfactual control groups used in this comparison. Overall, the analysis presented in this dissertation does show statistically significant positive economic impacts, on average, for rural U.S. Midwest counties with newly operational ethanol biorefineries relative to control counties without an ethanol biorefinery. These results demonstrate that the Renewable Fuel Standard (RFS) contributed to positive rural economic development impacts in treated counties with the possibility of spillover effects positively affecting contiguous counties.