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Why do many households remain exposed to large exogenous sources of nonsystematic income risk? We use a series of randomized field experiments in rural India to test the importance of price and nonprice factors in the adoption of an innovative rainfall insurance product. Demand is significantly price sensitive, but widespread take-up would not be achieved even if the product offered a payout ratio comparable to US insurance contracts. We present evidence suggesting that lack of trust, liquidity constraints, and limited salience are significant nonprice frictions that constrain demand.We suggest possible contract design improvements to mitigate these frictions.

This paper estimates how experimentally-manipulated experiences with a novel financial product, rainfall index insurance, affect subsequent insurance demand. Using a seven-year panel, we develop three main findings. First, recent experience matters for demand, consistent with overinference from small samples. Second, spillovers also matter, in the sense that the recent payout experience of village co-residents affects insurance demand about as much as one's own recent payout experience. Third, the spillover effect decays as time passes while the effect of one's own experience does not. We discuss implications of this analysis for commercial sustainability of this complicated but promising risk management technology.

The escalating wartime risks in Ukraine has led to a rapid reduction in insurance coverage in life and non-life segments. The purpose of the study is to rank Ukraine’s insurance companies based on an adaptation of the DEA method to the insurance market conditions. The study utilized the Supervisory Statistics of the National Bank of Ukraine as data. The study also used ranking of insurance companies based on technical efficiency criteria. The output indicators include the profitability of total capital (Output1) and the occupied share of the insurance market (Output2). The input indicators comprise the volume of total assets of insurers (Input1), the share of equity capital in assets (Input2), the level of gross payments (Input3), and the level of payments to insurance reserves (Input4). The ranking of insurance companies is based on minimizing the distance of each from the bounds of technical efficiency by solving a set of optimization problems. Based on the modeling results, a list of market leaders (7 companies) was formed for the end of the third quarter of 2023. They served nearly 37% of the insurance market in Ukraine, accumulating 41.5% of the total assets of this financial market sector. So, the primary issue for insurance companies is the loss of solvency due to the absence of adequate levels of insurance reserves. Therefore, priority measures should include strengthening regulatory constraints in this financial market segment to facilitate its qualitative renewal.

Take-up of an innovative rainfall insurance policy offered to smallholder farmers in rural India decreases with basis risk between insurance payouts and income fluctuations, increases with household wealth, and decreases with binding credit constraints. These results are consistent with the predictions of a simple neoclassical model with borrowing constraints. Other patterns are less consistent with the benchmark model. For example, participation in village networks and measures of familiarity with the insurance vendor are strongly correlated with insurance take-up decisions, and risk-averse households are less, not more, likely to purchase insurance. These results may reflect household uncertainty about the product, given their limited experience with it.

There is a wealth of literature on the design of ex post compensation mechanisms for natural disasters. However, more research needs to be done on the manner in which these mechanisms could steer citizens toward adopting individual-level preventive and protection measures in the face of flood risks. We have provided a comparative legal analysis of the financial compensation mechanisms following floods, be it through insurance, public funds, or a combination of both, with an empirical focus on Belgium, the Netherlands, England, and France. Similarities and differences between the methods in which these compensation mechanisms for flood damages enhance resilience were analyzed. The comparative analysis especially focused on the link between the recovery strategy on the one hand and prevention and mitigation strategies on the other. There is great potential within the recovery strategy for promoting preventive action, for example in terms of discouraging citizens from living in high-risk areas, or encouraging the uptake of mitigation measures, such as adaptive building. However, this large potential has yet to be realized, in part because of insufficient consideration and promotion of these connections within existing legal frameworks. We have made recommendations about how the linkages between strategies can be further improved. These recommendations relate to, among others, the promotion of resilient reinstatement through recovery mechanisms and the removal of legal barriers preventing the establishment of link-inducing measures.

Using U.K. microeconomic data, we analyze the empirical determinants of participation in the life insurance market. We find that term insurance demand is positively correlated with measures of bequest motives like being married, having children, and/or subjective measures of strong bequest motives. We then show that a life-cycle model of life insurance demand, saving, and portfolio choice can rationalize quantitatively the data in the presence of a bequest motive. These findings provide evidence supporting the presence of a bequest motive.

Purpose: Examine the relationship among assistive technology (AT), race, and other demographic characteristics (eg, sex, educational attainment, and employment status), medical coverage, as well as government support for veterans with severe disabilities. Methods: Data were analyzed from the Rehabilitation Services Administration-911 dataset collected in the United States in 2012. Descriptive and multivariate analyses were performed. Results: Among veterans with severe disabilities, factors that increased AT usage for European Americans (EAs) and African Americans (AAs) were employment, medical coverage (eg, access to private insurance and Medicare), government support (eg, Supplemental Security Income or Social Security Disability Income), and having a sensory type of disability. Having a mental disability was associated with decreased use of AT for EAs and AAs. Whereas EAs) had more factors associated with a decreased likelihood of using AT, AAs had more factors associated with an increased likelihood. For EA veterans with severe disabilities, receiving Medicaid, veteran benefits, and Workers' Compensation were associated with a decreased likelihood of using AT. AA veterans with severe disabilities were 60% less likely to use AT compared with EAs. Conclusions: Racial differences between AAs and EAs were observed in the use of AT by veterans with severe disabilities based on sex, education, employment status, medical coverage, and government support. AAs were generally less likely to use AT; however, greater exposure to resources such as employment, insurance, and government income support partially explained the differential AT use between EAs and AAs. Future policies and research should address these racial disparities in veterans with severe disabilities to promote equitable AT use and healthy functioning.

Unlike studies that estimate managerial bias, we utilize a direct measure of managerial bias in the U.S. insurance industry to investigate the effects of executive compensation and corporate governance on firms' earnings management behaviors. We find managers receiving larger bonuses and stock awards tend to make reserving decisions that serve to decrease firm earnings. Moreover, we examine the monitoring effect of corporate board structures in mitigating managers' reserve manipulation practices. We find managers are more likely to manipulate reserves in the presence of particular board structures. Similar results are not found when we employ traditional estimated measures of managerial bias.

Studies using pre-1990 data generally found benefit and frequency elasticities for workers' compensation cash benefits that exceeded, respectively, 1.0 and 0: an increase in expected benefits apparently induced (a) an even greater increase in actual benefit payments and (b) an increase in claim frequency. Researchers previously hypothesized that incentive effects for workers dominated those for employers. The authors of this study reevaluate benefit and frequency elasticities for 1975-89, using data with some advantages over those used by previous studies, and also investigate whether the elasticities changed during the years 1990-1999, when insurance policies with large déductibles increased employers' incentives to limit benefits and many states restricted benefit eligibility. For both periods, they find benefit elasticities significantly under 1.0 and frequency elasticities of about 0. They also find that much of the substantial decline in actual benefits in the 1990s was due to changes in state compensability rules and administrative stringency.

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.