To what extent do rising homeowners insurance premiums reflect growing climate risk? We examine hurricane risk in Florida and how insurers price it. Insurers rely on proprietary, third-party catastrophe models that estimate property-level expected losses. Using data from these models, we describe estimated hurricane risk across the state and how it evolved from 2006 to 2023. Expected property losses from hurricanes increased by 50%. Turning to insurance prices, we collect the premium rate filing data for insurers representing 70% of the total market. Insurers wanting to adjust their hurricane premium rates must file a request with the state regulator and justify the change with hurricane model output. Leveraging discrete updates to hurricane models, we estimate that a $1 increase in expected losses increases hurricane premiums by $5.82. Overall, hurricane premiums increased by more than 200% during our period of study. We consider potential mechanisms and find that the large markup above expected loss appears to reflect 1) reliance in the insurance market on local, limitedly diversified insurers, paired with 2) the rising cost of reinsurance.

In catastrophe-exposed insurance markets, a large share of what homeowners pay reflects the cost of correlated tail risk. Local insurers cannot diversify these losses on their own and instead transfer them to globally diversified reinsurers, but reinsurance is expensive and volatile. We study a regulatory reform that decreased the capital cost of reinsuring tail risk. After the cost decrease, reinsurance use expanded by 25%, the reinsurance market became less concentrated, and the model-implied price of reinsurance fell by a third. By reducing required capital, the reform also increased insurers' exposure to reinsurer non-payment risk. We show this dramatically impacted the primary insurance market in hurricane-exposed areas of Florida: additional insurers entered and the cost of insuring wind risk fell sharply. To quantify welfare, we estimate an equilibrium model of the Florida wind-insurance market. The model implies that the reform reduced treated insurers' marginal cost of supplying wind coverage by about 10% and lowered equilibrium premiums by about 14%. Counterfactual simulations imply consumer-surplus gains of $186 per household per year, split roughly equally across direct cost pass-through, strategic markup adjustment, and expanded product availability. Our results imply that capital costs required to cover tail events, over and above average losses, meaningfully contribute to consumers' premiums and that policies that reduce those capital costs can decrease prices and improve availability for homeowners insurance.

How do collateral requirements impact consumer borrowing behavior? Using administrative loan application and performance data from the U.S. Federal Disaster Loan Program, we exploit a loan amount threshold above which households must post their residence as collateral. Our bunching estimates suggest that the median borrower is willing to give up 40% of their loan amount to avoid posting collateral. Exploiting time variation in the threshold, we estimate collateral causally reduces default rates by 36%. Finally, we structurally estimate households' attachment to their homes, net of any equity, and find a median value of $11,000. Attachment creates a wedge between lender and borrower valuation of collateral of 15%. Our results explain high perceived default costs in the mortgage market, and document the importance of collateral for reducing moral hazard in consumer credit markets.

Does emergency credit prevent long-term financial distress?We study the causal effects of government-provided recovery loans to small businesses following natural disasters.The rapid financial injection might enable viable firms to survive and grow or might hobble precarious firms with more risk and interest obligations.We show that the loans reduce exit and bankruptcy, increase employment and revenue, unlock private credit, and reduce delinquency.These effects, especially the crowding-in of private credit, appear to reflect resolving uncertainty about repair.We do not find capital reallocation away from neighboring firms and see some evidence of positive spillovers on local entry.

We examine the effects of a severe climate event on local firms. Our data include 8,218 business credit reports and a detailed survey of 273 businesses in the area affected by Hurricane Harvey. Delinquent credit balances doubled in areas with the worst flooding, although nonflooded areas also had significant credit impairments. Only independent businesses showed signs of distress; subsidiaries of larger firms did not. Firms were largely uninsured and often were denied credit postdisaster. Many funded recovery informally, such as through friends and family. Our findings suggest that several financial frictions compound the challenges posed by a severe climate event.

We estimate and trace a credit demand curve for households that recently experienced damage to their homes from a natural disaster. Our administrative data include over one million applicants to a federal recovery loan program for households. We estimate extensive‐margin demand over a large range of interest rates. Our identification strategy exploits 24 natural experiments, leveraging exogenous, time‐based variation in the program's offered interest rate. Interest rates meaningfully affect consumer demand throughout the distribution of rates. On average, a 1 percentage point increase in the interest rate reduces loan take‐up by 26%. We find a large impact of applicants' credit quality on demand and evidence of monthly payment targeting. Using our estimated demand curve and information on program costs, we find that the program generates an average social surplus of $2900 per borrower.

What are the causal effects of emergency credit on households’ finances after a negative shock? We link U.S. Federal Disaster Loan application data to applicants’ credit records before and after a natural disaster. Using an instrumented difference-in-differences research design exploiting a discontinuity in underwriting, we find that credit provision significantly reduces severe financial distress. We explore mechanisms using additional quasi-experimental variation in interest rates, finding support for a liquidity-based explanation. Disaster loan provision also has real effects in the form of additional car purchases. Well-timed liquidity provided to households in acute need has substantial and persistent positive effects.

Homeowners are engaging with climate risk through their insurance premiums. We examine high-risk, heavily-subsidized flood insurance policyholders. A reform increased their premiums but also informed them that premiums would not exceed the actuarially fair price. We estimate that 26% of homeowners stopped insuring in response. Relocation and risk mitigation explain up to half of households’ nonrenewals. The other half is less adaptive: households appear to drop their insurance, going unprotected. We find a negative welfare impact of the premium increases due to more households living in uninsured, unmitigated homes. Our findings highlight challenges in using insurance price signals as a stand-alone policy tool to steer climate adaptation.

Households' insurance coverage against severe losses is central to their financial resilience. Features of the US National Flood Insurance Program offer insights into consumers' coverage over large stakes that are not typically possible in other markets. We examine the coverage limits (the amount of a home's value that is insured) of over 100,000 households. We determine the optimal coverage based on a standard expected utility calibration. This model indicates that consumers should purchase a low limit and retain much of their exposure due to the high premium loads in our sample. Instead, households in the sample typically fully insure their homes, paying premiums well above their contract's expected value. We investigate possible explanations for homeowners' coverage limits and conclude that some combination of industry practices that emphasize fully insuring and probability distortions in decision-making are likely explanations.

Negative shocks to housing, most households’ largest consumption good, are expected to create strong credit demand to smooth these shocks over time. We estimate and trace a credit demand curve for households who recently experienced a negative shock to their housing stock. We use administrative data on over one million applications to a federal loan program for households impacted by natural disasters. Our identification strategy exploits 24 quasi-experiments, leveraging exogenous, time-based variation in the program's offered interest rate to estimate extensive-margin demand. We find that households are surprisingly price-sensitive, only a third would accept loans offered at the 30-year mortgage rate. We find a large impact of credit quality on demand and evidence of monthly payment targeting. Credit-constrained households exhibit inelastic demand. Many high credit quality applicants are reluctant to borrow, even at very low interest rates where no private alternative exists.

Economists, regulators, and consumer protection agencies have highlighted the welfare losses for consumers who purchase high-load insurance against modest stakes risks. Mandatory information disclosure is a potentially attractive public policy tool that might improve consumers' choices, but has not been widely tested in insurance settings. We conduct an incentive-compatible insurance demand experiment in which we manipulate the information disclosed to subjects. We test whether any of the three most commonly suggested disclosures affect insurance demand, disclosing either (1) the true probability of loss, (2) the contract's expected loss, or (3) the insurer's profit on the transaction. Similar to consumers in naturally-occurring insurance markets, subjects in the laboratory demonstrate significant demand for high-load insurance against modest stakes. However, we find no effect of any of the three disclosure treatments on subjects' insurance choices. We discuss the implications of our results for possible public policy initiatives in insurance markets.

We examine the ability of insurers to influence the coverage limit decisions of 180,000 households in the National Flood Insurance Program. In this program, private insurers sell identical flood contracts at identical rates and bear no risk of paying claims. About 12 percent of new policyholders overinsure, selecting a coverage limit that exceeds their home's estimated replacement cost. Overinsuring is expensive relative to expected loss, making it difficult to explain with standard decision‐making models. The rate of overinsuring differs substantially across insurers, ranging from zero to one‐third of new policies. Insurer effects on the likelihood of overinsuring are statistically significant after controlling for the policyholder's characteristics. Additionally, some insurers seem to encourage households to overinsure in percentage terms (e.g., buy 110 percent of replacement cost) while others encourage rounding up in dollars (e.g., to the next $ 10,000). We find that insurers’ distribution systems and commission rates influence whether their policyholders overinsure.

We examine businesses' financial management of a rare, severe event using detailed firm-level data collected following Hurricane Sandy in the New York area.Credit played a prominent role in financing recovery; more negatively affected firms took on debt because of Sandy (38%) than received insurance payments (15%) in our data.Negatively affected firms were often credit constrained after the shock.While firms' demand for insurance is often explained by financing frictions, we find that the most credit constrained firms after the event, younger firms and smaller firms, were the least likely to insure before it.

This paper considers lender-level index insurance as a means of expanding access to credit in disaster-prone communities. In this approach, the lender transfers the disaster risk of loans in its portfolio by contracting on an observable measure of the catastrophe. I develop and calibrate a dynamic, stochastic model using data from a community lender in Peru that is vulnerable to El Niño-related flooding. The modeled lender can insure against El Niño using an index-based product that is available for purchase by financial intermediaries in Peru. I examine how premium rates, basis risk, and background risk may in influence the lender's insurance decision and credit supply. Overall, the results suggest that lender-level index insurance holds promise for reducing disaster-related credit supply shocks and expanding credit access in vulnerable communities.

Financing frictions may limit the risk management of businesses, increasing their vulnerability toshocks. We examine business financing outcomes following Hurricane Harvey. Our analyses usetwo novel datasets on private companies: the credit reports of 8,219 businesses and a survey of273 local firms. We address two questions in our analyses. First, to what extent did Harvey causefirms financial distress? Using their exact street addresses, we match businesses' credit reportswith flood depths from Harvey in difference-in-differences estimations. Flooded firms fell behindon their debt obligations, though these businesses avoided the most serious credit outcomes suchas bankruptcy. Only independent businesses show signs of distress; subsidiaries of larger firmsdo not. Second, how did firms finance losses from Harvey? Firms were largely uninsured fortheir losses and were often denied credit after Harvey. Many funded recovery through informalmeans, such as friends and family financing. Our study highlights and quantifies the challengesposed by financing frictions in the wake of a negative shock.

How households will respond to reforms of public insurance programs is unclear given recent behavioral findings on consumers’ insurance choices. We examine the insurance decisions of an extremely vulnerable group in the U.S. National Flood Insurance Program. Severe repetitive loss (SRL) properties account for only 1% of policies but 25–30% of flood claims. Congress passed a reform that phases out the premium subsidies offered to this group over several years such that their premiums will eventually equal their contract’s actuarially fair rate. We measure the effect of the reform using difference-in-differences estimation on a panel of over two million policy-year observations. We find that about one fourth of SRL property owners decided to stop insuring in response to the reform. The reform did not meaningfully affect the coverage limit choices of households that continued to insure. Curiously, the observed effect on nonrenewal begins after the law was ratified but before it was implemented. Our findings thus seem in contrast to canonical and most common behavioral theories of insurance demand. We discuss potential alternative decision-making explanations of our results and are able to rule out some of them. Our findings add to research on public policy design and behavioral insights into insurance demand.

OF THESIS DOES OPTIMISM EXPLAIN HOW RELIGIOUSNESS AFFECTS ALCOHOL USE AMONG COLLEGE STUDENTS? Alcohol use, because of its many negative consequences, is the number one health problem facing college students. Because of this, researchers have looked for factors associated with reduced drinking. Religiousness is one such factor. Religiousness is a complex, multidimensional construct, and while it tends to be negatively associated with alcohol use, research progress has been slow due to the tendency of researchers to poorly operationalize this construct and to design studies that fail to go beyond the bivariate relationship of religiousness and alcohol use. In order to address these shortcomings, this study will assess two dimensions of religiousness, religious commitment/motivation and religious consequences, and will test a model, presented by Koenig et al., (2001), that postulates religiousness works through mental health in order to reduce alcohol use. More specifically, this study will test optimism as a possible mediator and moderator of the relationship between religiousness and alcohol use. This study used archival data from 260 (202 female and 58 male) Caucasian, Christian, undergraduate college students who completed a battery of surveys that included measures of religiousness, optimism, and alcohol use. A factor analysis was conducted on one measure of religiousness, the short form of the Faith Maturity Scale. Also, optimism was tested as both a mediator and a moderator for both dimensions of religiousness in predicting alcohol use. Findings indicated optimism is not a significant mediator of the religiousness-alcohol use relationship because optimism did not meet the preconditions for a mediator as it was not associated with alcohol use in this sample. Also, optimism was not a significant moderator of religious commitment/motivation, but it did moderate the relationship of religious consequences and alcohol use. Finally, the two dimensions of religiousness interacted in predicting alcohol use. While both dimensions of religiousness were negatively associated with alcohol use throughout the findings, gender was a significant moderator in all significant interactions. Several implications follow from this study. First, greater specificity is needed regarding Koenig et al.’s (2001) model specifically in regards to which third variables are associated with which health outcomes and to whom the model applies. Second, this study highlights the importance of a multidimensional assessment of religiousness. Finally, this study indicates specificity is needed regarding what religious interventions will be helpful for which genders.

We examine the flood insurance decisions of over 100,000 households, using standard expected utility models and rank dependent utility models that incorporate probability distortions. Consumers’ insurance choices provide important insights into their risk attitudes. Previous research has typically examined modest stakes choices, such as appliance warranties and deductibles, leaving important questions about larger stakes decisions. Features of U.S. flood insurance allow us to model risk attitudes over large stakes from consumers’ coverage limits. We find that consumers are typically willing to pay premiums well above the expected value of their contracts, though consumers’ decisions vary substantially over large stakes. Explaining these choices with standard expected utility models requires massive variation in risk aversion. In contrast, models incorporating probability distortions greatly improve the ability to predict households’ decisions. These models explain consumers’ choices through their overweighting of small probabilities and can more easily accommodate the observed variation in consumers’ decisions. Our large-stakes choice analyses reveal new insights on models of consumers’ risk preferences.

Credit provides a means for uninsured households and businesses to manage disaster losses, but access to credit may be tenuous after severe events. Using lender fixed effects models, we examine how natural disasters affect the amount of credit supplied by community lenders in developing and emerging economies. We find that disasters reduce lending. We consider two potential causes of lending reductions: 1) disasters reduce expected returns on loans made after the event or 2) capital constraints, lenders' difficulty replacing equity lost during the event. We develop a dynamic model that informs our empirical identification of these causes and conclude that capital constraints cause observed credit contractions. We also examine the effects of insurance market development and find evidence that insurance preserves the creditworthiness of borrowers. Our results demonstrate pervasive disaster-related credit supply shocks in developing and emerging economies and identify new insurance market opportunities.