Session 9: Market Failures and Public Policy

Student loans with income-contingent repayment insure borrowers against income risk but can reduce their incentives to earn more. Using a change in Australia’s income-contingent repayment schedule, I show that borrowers reduce their labor supply to lower their repayments. These responses are larger among borrowers with more hourly flexibility, a lower probability of repayment, and tighter liquidity constraints. I use these responses to estimate a dynamic model of labor supply with frictions that generate imperfect adjustment. My estimates imply that the labor supply responses to income-contingent repayment decrease the optimal amount of insurance but are too small to justify fixed repayment contracts. Moving from a fixed repayment contract to a constrained-optimal income-contingent loan increases welfare by the equivalent of a 1.3% increase in lifetime consumption at no additional fiscal cost.

This paper develops a novel method to empirically analyze competitive equilibrium in selection markets when firms offer differentiated products while having different cost structures and information precision. We apply the method to study a representative sample of Italian auto insurance contracts and associated claims from 2013 to 2021. We find substantial differences in the precision of risk rating across insurers, and companies with less accurate risk rating algorithms tend to have more efficient cost structures. If all insurers were to counterfactually adopt the least advanced information technology (under more stringent privacy regulation), average consumer surplus would increase by 4.5%, and the gain primarily comes from high risk drivers. Allowing all firms to share the best risk rating technology improves the efficiency of the insurer-insuree match: the cost to insure consumers decreases by 4 euros per contract, and high-risk consumers are more likely to be covered by insurers with lower claim processing costs.

Making expensive, innovative drugs affordable and accessible is a pressing global challenge. In this paper, we explore the welfare and equity effects of a recent policy reform in China that coupled centralized drug price negotiation with expanded insurance coverage. Before the reform, innovative drugs were mostly excluded from coverage, leading to high out-of-pocket expenses for consumers. The reform offered insurance coverage for these drugs, together with price reductions negotiated between the government and drug producers. Our design-based analysis shows that successful negotiations were followed by 48% decreases in list prices and 350% increases in utilization. The reform also coincided with increased entry of innovative drugs into the Chinese market. We build a flexible model of demand and supply for cancer drugs to understand the mechanisms driving these results.

Our model estimates and counterfactual simulations yield three key insights. First, there’s a notable synergy between insurance expansion and price negotiation. Insurance coverage incentivizes drug produers to engage in negotiations, which reduces costs and increases consumer surplus gains by 77% relative to scenarios where drugs are covered without negotiation. Second, the current coinsurance design tends to favor residents in wealthier provinces, thus benefiting higher-income consumers more. We present the results of alternative coinsurance models with equity targets. Lastly, we consider the impact of decentralized, province-level negotiations on the effectiveness of this policy.

Consumer subsidies are a common policy tool for supporting the adoption of clean energy technologies. Policymakers often justify these programs as a means of stimulating infant industries, arguing that greater demand increases industry learning-by-doing, which in turn reduces costs for potential entrants. This requires learning spillovers between firms that make experience-based cost reductions a public good. However, spillovers can reduce firms’ incentives to expand output and lower costs. To evaluate this tradeoff, I estimate a dynamic structural model of the market for solar panel installations in California that endogenizes firms’ entry and exit decisions and allows for learning-by-doing with knowledge spillovers. I find that a 1% increase in a firm’s cumulative production leads to a 0.7% reduction in installation-specific costs and that learning spills over across firms. Counterfactual analysis reveals that a state-level consumer subsidy program increased solar adoption by 4% and installer entry by 9%, indicating that industry cost reductions outweigh any strategic incentives for firms to reduce learning. While consumer subsidies achieve industry growth, I find that standard industrial policies such as entry subsidies provide greater welfare gains.

This paper investigates the dynamic efficiency of policy uncertainty in the US wind energy industry. Policy expiration embedded in the Production Tax Credit induced uncertainty among wind farm investors and expedited investment. I compile a comprehensive data set of the investment, production, and long-term contracts on the US wind energy market. I find significant bunching in the number of new wind farms at the expiration dates of the short policy windows and a large mismatch among wind farm investment timing, continuously improving upstream turbine technology, and evolving demand for wind energy. I then develop an empirical model featuring the bilateral bargaining of long-term contracts, endogenous buyer matching, and dynamic wind farm investment under policy uncertainty. Model estimates reveal that a lapse in policy extension reduced the perceived likelihood of policy renewal to 30%, and counterfactual simulations demonstrate that removing policy uncertainty postpones the entry of 53% of the affected cohort by 3.5 years. Removing policy uncertainty increases the net social surplus by 5.9 billion dollars and could save fiscal expenditure without sacrificing social welfare.

We characterize optimal urban transportation policies in the presence of congestion and environmental externalities and evaluate their welfare and distributional effects. We present a framework of a municipal government that implements different transportation equilibria through its choice of public transit policies—prices and frequencies—as well as road pricing. The government faces a budget constraint that introduces monopoly-like distortions. We apply this framework to Chicago, for which we construct a new dataset that comprehensively captures transportation choices. We find that road pricing alone leads to large welfare gains by reducing externalities, but at the expense of consumers (travelers), whose surplus falls even if road pricing revenues are fully rebated. The largest losses are borne by middle income consumers, who are most reliant on cars. We find that the optimal price of public transit is close to zero and goes along with a reduction in the frequency of buses and an increase in the frequency of trains. Combining these transit policies with road pricing eliminates budget constraints. This allows the government to implement higher transit frequencies and even lower prices, in which case consumer surplus increases after rebates.

Healthcare is often allocated without prices, sacrificing efficiency in the interest of equity. Wait times then typically serve as a substitute rationing mechanism, creating their own distinct efficiency and distributional consequences. I study these issues in the context of the Veterans Health Administration (VA), which provides healthcare that is largely free but congested, and the Choice Act, a large-scale policy intervention that subsidized access to non-VA providers to reduce this congestion. Using variation in Choice Act eligibility in both patient-level and clinic-level difference-in-differences designs, I find that the price reduction for eligible veterans led to substitution away from the VA, an increase in overall healthcare utilization and spending, and reduced wait times at VA clinics in equilibrium. I then use the policy-induced price and wait time variation to estimate the joint distribution of patients’ willingness-to-pay and willingness-to-wait. I find that rationing via wait times redistributes access to healthcare to lower socioeco- nomic status veterans, but at a large efficiency cost (-23%). By contrast, I find that a coarsely targeted, modest increase in copayments increases consumer surplus by more than the Choice Act, at lower cost to the VA, while disproportionately benefitting low-income veterans.

In the face of limited health care resources, waiting time often serves as a rationing mechanism in health systems around the world. We evaluate the efficiency and equity consequences of rationing via queues in the context of surgical care. Focusing on the U.S. private insurance market, we first develop a new measure of wait time that captures the complete patient trajectory—including visits for primary care, laboratory testing, and medical imaging—along the path to surgery. Exploiting exogenous variation in these waits due to weekly congestion within a patient’s insurance network, we show that patients who wait a month more spend 5.9% more on hospital care, are 3.1% more likely to be readmitted to a hospital, and fill 6.6% more opioid prescriptions in the six months following a surgery. We further demonstrate misallocation of wait times relative to the planner’s ideal: we identify heterogeneous effects of waiting, but those patient groups who suffer the highest costs from delay do not uniformly experience shorter waits. For a set of common surgeries, we illustrate how reassigning patient priorities in the queue—say, by providing physicians information on the treatment effects of waiting by patient type—could substantially reduce payments to hospitals and improve overall patient health outcomes.   

Many centralized assignment systems seek to not only provide good matches for participants’ current needs, but also to accommodate changing preferences and circumstances. We study the problem of designing such a mechanism in the context of Norway’s system for dynamically allocating patients to general practitioners (GPs). We provide direct evidence of misallocation under the current system––patients sitting on waitlists for each others’ GPs, but who cannot trade––and propose an alternative mechanism that adapts the Top-Trading Cycles (TTC) algorithm to a dynamic environment. Because of patients’ dynamic incentives, dynamic TTC raises novel incentive and distributional concerns relative to the static case. We then estimate a structural model of switching behavior and GP choice and empirically evaluate how this mechanism would perform relative to the status quo. While introducing TTC would on average reduce waiting times and increase patient welfare—with especially large benefits for young and female patients—patients endowed with undesirable GPs would be harmed. Adjustments to the priority system can avoid harming this group while preserving most of the gains from TTC.

Carbon offsets could reduce the global costs of carbon abatement but there is little evidence on how much they truly reduce emissions. We study carbon offsets sold by firms under the Clean Development Mechanism (CDM) in China by matching offset projects proposed to the United Nations to panel data on emissions and output for manufacturing firms. We have two main findings. First, the CDM attempts to screen out projects that would be profitable without offset payments by rejecting proposed projects with higher stated returns. Second, offset-selling firms steeply increase emissions after registering an offset project, relative to similar firms that proposed a project but did not follow-through. We explain this increase in emissions by jointly modeling the firm decision to propose an offset project and the Board’s decision of whether to approve. In the model, CDM firms increase emissions due to a combination of the selection of higher-growth firms into abatement project investment and the causal effect of higher productivity, post investment, on firm scale and therefore emissions.

We evaluate the extent to which Google’s dominance in web search is driven by higher quality, default effects, and/or economies of scale in data. We develop a model of consumer choice with quality preferences, inertia, and learning effects, and estimate it using a randomized experiment with desktop internet users. Facilitating active choice between search engines has almost no effect on market shares, but changing the address bar default has persistent market share effects of about 25 percent after four weeks in pilot data. Early results show that remarkably, paying Google users to try Bing for two weeks causes about 20 percent to stay with Bing afterwards, while paying Bing users to try Google causes a similar share to stay with Google. Using internal Microsoft search logs, we estimate that Bing’s click- through rates on less-common search queries improve by 7 percent when the amount of data doubles. In counterfactual simulations, choice screens have very limited effects, switching Chrome defaults to Bing reduces welfare, and sharing Google’s click-and-query data with Microsoft increases Bing’s market share only slightly.

This paper investigates the impact on consumers of the widespread manipulation of reputation systems by sellers on two-sided online platforms. We focus on a relevant empirical setting, the use of fake product reviews on e-commerce platforms, which can affect consumer welfare via two channels. First, rating manipulation deceives consumers directly, causing them to buy lower quality products and pay higher prices for the products with manipulated ratings. Second, the presence of rating manipulation lowers trust in ratings, which may result in worse product matches if consumers place too little weight on quality ratings. This decrease in trust may also increase price competition and benefit consumers by lowering prices on high quality products whose quality is less easily observed. We formally model how consumers form beliefs about quality from product ratings and how these beliefs are affected by the presence of fake reviews. We use incentivized survey experiments to measure beliefs about fake review prevalence. Our model of product quality is incorporated into an empirical model of consumer demand for products and how demand is shifted by ratings, reviews, and prices. The model is estimated using a large and novel dataset of products observed buying fake reviews to manipulate their Amazon ratings. We use counterfactual policy simulations in which fake reviews are removed and consumer beliefs adjust accordingly to explore the effectiveness and welfare and profit implications of different methods of regulating fake reviews.

In many online markets, platforms engage in platform design by choosing product recommendation systems and selectively emphasizing certain product characteristics. I analyze the welfare effects of personalized recommendations in the context of the online market for hotel rooms using clickstream data from Expedia Group. This paper highlights a tradeoff between match quality and price competition. Personalized recommendations can improve consumer welfare through the “long-tail effect,” where consumers find products that better match their tastes. However, sellers, facing demand from better-matched consumers, may be incentivized to increase prices. To understand the welfare effects of personalized recommendations, I develop a structural model of consumer demand, product recommendation systems, and hotel pricing behavior. The structural model accounts for the fact that prices impact demand directly through consumers’ disutility of price and indirectly through positioning by the recommendation system. I find that ignoring seller price adjustments would cause considerable differences in the estimated impact of personalization. Without price adjustments, personalization would increase consumer surplus by 2.3% of total booking revenue (~$0.9 billion). However, once sellers update prices, personalization would lead to a welfare loss, with consumer surplus decreasing by 5% of booking revenue (~$2 billion).