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We experimentally varied information mailed to 87,000 households in California’s health insurance marketplace to study the role of frictions in insurance take-up. Reminders about the enrollment deadline raised enrollment by 1.3 pp (16 percent) in this typically low take-up population. Heterogeneous effects of personalized subsidy information indicate misperceptions about program benefits. Consistent with an adverse selection model with frictional enrollment costs, the intervention lowered average spending risk by 5.1 percent, implying that marginal respondents were 37 percent less costly than inframarginal consumers. We observe the largest positive selection among low income consumers, who exhibit the largest frictions in enrollment. Finally, we estimate the implied value of the letter intervention to be $25 to $53 per month in subsidy dollars. These results suggest that frictions may partially explain low take-up for marketplace insurance, and that interventions reducing them can improve enrollment and market risk in exchanges.

We propose a robust method of discrete choice analysis when agents’ choice sets are unobserved. Our core model assumes nothing about agents’ choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model’s parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households’ risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non-singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher-dimensional unobserved heterogeneity.

Using data from a two-year pricing experiment, we study the impact of subsidy policies on weather insurance take-up. Results show that subsidies increase future insurance take-up through their influence on payout experiences. Exploring mechanisms of the payout effect, we find that for households that randomly benefited from financial education, receiving a payout provides a one-time learning experience that improves take-up permanently. In contrast, households with poor insurance knowledge continuously update take-up decisions based on recent experiences with disasters and payouts. Combining subsidy policies with financial education can thus be effective in promoting long-run insurance adoption.

This paper provides experimental support for the hypothesis that insurance can be a motive for religious donations. We randomize enrollment of members of a Pentecostal church in Ghana into a commercial funeral insurance policy. Then church members allocate money between themselves and a set of religious goods in a series of dictator games with significant stakes. Members enrolled in insurance give significantly less money to their own church compared to members that only receive information about the insurance. Enrollment also reduces giving towards other spiritual goods. We set up a model exploring different channels of religiously based insurance. The implications of the model and the results from the dictator games suggest that adherents perceive the church as a source of insurance and that this insurance is derived from beliefs in an interventionist God. Survey results suggest that material insurance from the church community is also important and we hypothesize that these two insurance channels exist in parallel.

This paper is concerned with learning decision-makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model’s semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.

Most individual life insurance policies lapse, with lapsers cross-subsidizing non-lapsers. We show that policies and lapse patterns predicted by standard rational expectations models are the opposite of those observed empirically. We propose two behavioral models consistent with the evidence: (i) consumers forget to pay premiums and (ii) consumers understate future liquidity needs. We conduct two surveys with a large insurer. New buyers believe that their own lapse probabilities are small compared to the insurer’s actual experience. For recent lapsers, forgetfulness accounts for 37.8 percent of lapses while unexpected liquidity accounts for 15.4 percent.

The objective of this paper is to propose a novel deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. It lays the groundwork for understanding how insights can be gained into the fraudulent behavior of an insured person with minimum effort. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Each model's dynamics is discussed to inform the reader on how models can be adapted for fraud detection and how results can be perceived appropriately. Both qualitative and quantitative performance evaluations are conducted, although a greater emphasis is placed on qualitative evaluation. To broaden the scope of reference of fraud detection setting, various metrics are used in the qualitative evaluation.

Nanotechnology is a new field that has gained considerable importance due to its potential uses in the field of biosciences, medicine, engineering, etc. In the present study, bio-inspired metallic iron nanoparticles (FeNPs) were prepared using biomass of Enterobacter train G52. The prepared particles were characterized by UV-spectroscopy, TGA, XRD, SEM, EDX, and FTIR techniques. The crystalline nature of the prepared FeNPs was confirmed by XRD. The SEM techniques revealed the particles size to be 23 nm, whereas in FTIR spectra the peaks in the functional group region indicated the involvement of bioactive compounds of selected bacterial strains in the capping of FeNPs. The EDX confirmed the presence of iron in the engineered FeNPs. The FeNPs were then evaluated for its antibacterial, antifungal, antioxidant, anti-inflammatory, anti-Alzheimer’s, anti-larvicidal, protein kinase inhibition, anti-diabetic, and biocompatibility potentials using standard protocols. Substantial activities were observed in almost all biological assays used. The antioxidant, anti-cholinesterase, and anti-diabetic potential of the prepared nanoparticles were high in comparison to other areas of biological potential, indicating that the FeNPs are capable of targeting meditators of oxidative stress leading to diabetes and Alzheimer’s disease. However, the claim made needs some further experimentation to confirm the observed potential in in vivo animal models.

The aim of this paper is to investigate the relevance of sustainable product attributes as compared to ongoing costs and risk–return profiles when individuals choose funds underlying unit-linked life insurances. Regarding sustainability attributes, we focus on the product classification according to the Sustainable Finance Disclosure Regulation as a European regulatory transparency standard, and on sustainable investment strategies. We conduct two choice-based conjoint analyses using a German panel for unit-linked life insurances as well as fund savings plans as a financial product comparison. We estimate the relative importance, part-worth utilities, and the marginal willingness to pay for changes in product attributes. Our results suggest that private investors of unit-linked life insurances value sustainable product attributes and that they result in a slightly higher marginal willingness to pay, but risk–return indicators and especially ongoing costs are currently more relevant. We find further indications that sustainability attributes are less relevant in the setting of a unit-linked life insurance as compared to a fund savings plans setting.

Workers’ compensation insurance, which provides no-fault coverage for work-related injuries, is mandatory in nearly all states. We use administrative data from a unique market without a coverage mandate to estimate the demand for workers’ compensation insurance, leveraging regulatory premium updates for identification. We find that a 1 percent increase in premiums leads to approximately a 0.3 percent decline in coverage. Drawing upon these estimates and data on costs, we examine potential justifications for government intervention to increase coverage. This analysis suggests that several forms of market failure—such as adverse selection, market power, and externalities—may not justify a mandate in this setting.