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This article proposes a methodology to calculate the effect of moral hazard on short term credit (working capital) to small and medium-sized enterprises (SMEs). The methodology incorporates four categories of moral hazard ratio defined in a previous study, which are employed to determine probability of default based on a logit model. To this end, a novel Colombian database is used to calculate a moral hazard index that considers the percentages of the odds ratios of the moral hazard variables for positive coefficients on the probabilities of default. The empirical analysis result in an index measuring the impact of moral hazard on odds ratio mainly based on underinvestment moral hazard category in the sample of analyzed companies for the period ranged from 2007 to 2014.

This paper models and investigates risk and security perception of metro travelers, in cases of malevolent, man-made incidents. Data is collected through an extensive survey in Athens, Greece; the survey includes an image-based stated-preference experiment, depicting different risk and security conditions. Econometric models are developed for estimating willingness to pay for mitigating perceived risk and for improving security in the Athens metro system. Results highlight the value that travelers attribute to improving security and avoiding threatening conditions in the Athens metro system, which is estimated to a 25%–30% of the system’s fare level. The paper provides useful insights on acceptable fare changes for improving security and risk perception in the Athens metro system.

We use detailed assessments of CEO personalities to explore the nature of CEO overconfidence as it is commonly measured. Longholder, the option-based measure of CEO overconfidence introduced by Malmendier and Tate (2005a) and widely used in the behavioral corporate finance and economics literatures, is significantly related to several specific characteristics that are associated with overconfident individuals as well as individuals with lower ability. Similar relations hold for overconfidence measures based on CEOs’ earnings guidance. Investment-cash flow sensitivities are larger for both Longholder and less able CEOs. After controlling for ability and other characteristics, Longholder CEOs’ investments remain significantly more sensitive to cash flows. These results suggest that overconfidence, as measured by Longholder, is correlated with lower ability but still reflects empirically distinct aspects of overconfidence.

Background In 2019, the total National Healthcare Expenditure (NHE) reached $3.8 trillion, or nearly 20% of the total Gross Domestic Product. This represents a 4-fold increase in its Gross Domestic Product share since 1960. Given the magnitude and growth of such expenditures, healthcare cost containment is central to the country's financial sustainability. Moreover, as Medicare represents nearly 20% of the total NHE, it is a primary target for piloting, implementing and scaling initiatives that reduce expenditures. Objective To assess trends in Medicare Physician Fee Schedule for Service (Part B) payments and utilization for Imaging relative to other services from 2009 through 2019. Materials and methods We conducted a retrospective observational study of annual expenditures and utilization of imaging services using data from Centers for Medicare and Medicaid Services Medicare Part B. Data was grouped according to the Berenson-Eggers Type of Service Classification. The values were adjusted for inflation and we calculated the annual changes and the compound annual growth rates and the price elasticity of supply. Results For the 10-year period, Imaging represented on average 8% of the total Medicare Part B expenditures, an equivalent of 1.4% of total Medicare expenditures or 0.05% of the NHE. While NHE, overall Medicare and overall Part B had positive growth rates of total expenditures, Imaging did not. Moreover, Imaging had the most negative CAGR compared to all other categories, including Drugs, Procedures, Evaluation and Management and Durable Medical Equipment. Conclusion Imaging did not contribute to the increase in Medicare Part B expenditures, and it might have been disproportionately affected by cost containment policies.

This paper analyzes the impact that health indicators have upon an individual's trip and mode choices to out-patient care facilities. The study's focus is an out-patient trip to a health care facility and the source of data is the China Health and Retirement Longitudinal Study (CHARLS) for 2011, 2013 and 2015. Based on a random utility framework, the study finds that making a trip to treat an illness or for a check-up increases the likelihood of an out-patient trip by 52 and 31 probability points, respectively. Out-patient visits for which insurance is not relevant, when the individual pays most of the out-of-pocket costs and when the facility is a public facility are also important factors. Diagnosed and other personal health factors have some but much more modest effects on one's trip choice. The analysis also identifies differential modal impacts of health indicators. A series of robustness tests generally confirms the results but also identifies areas for further research. Including a no-trip option, the biannual survey and infrequent out-patient trip-making mitigate endogeneity concerns. The analysis has broad health policy and transportation implications for an ageing population whose share is increasing.

In the present work, we study the problem of optimal management of defined contribution pension funds, during the distribution phase, under the effect of inflation, mortality and model uncertainty. More precisely, we consider a class of employees, who, at the time of retirement, enter a life assurance contract with the same insurance firm. The fund manager of the firm collects the entry fees to a portfolio savings account and this wealth is to be invested optimally in a Black–Scholes type financial market. As such schemes usually last for many years, we extend our framework, by: (i) augmenting the financial market with an inflation-adjusted bond, and, (ii) taking into account mortality of the fund members. Model uncertainty aspects are introduced as the fund manager does not fully trust the model he/she faces. By resorting to robust control and dynamic programming techniques, we provide: (a) closed-form solutions for the case of the exponential utility function, (b) a detailed study of the qualitative features of the problem at hand that elucidates the effect of robustness and inflation on the optimal investment decisions.

Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible-Infected-Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic.

The global shift towards defined-contribution pension schemes has been accompanied by asymmetric risks and new responsibilities for households to plan and fund effectively their own retirement over the years. In this study, expressing and combining preferences for consumption, investment, bequest, public pension entitlement and the choice of reverse mortgage products, we develop several utility-based life cycle models to facilitate the complex decision-making process that retired households are required to follow to optimise their retirement income. This optimal policy is given in the form of either an analytical or a numerical solution using stochastic dynamic programming. The timing of this paper coincides with the launch of a reverse mortgage style loan, offered by the Australian federal government and allowing retired households to receive an income stream by taking out a loan against the equity in their home. Calibration is performed using real Australian household data.

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thromobocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate the existing evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate the existing evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the structure of the model, relevant variables, data to be included, and how these data were used to inform the model.The model can be used as a decision support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it a useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be easily adaptable to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.

In Chapter 11 bankruptcy cases in the United States, firms are either reorganized, acquired, or liquidated. I show that decisions to liquidate often reduce creditor recovery, costing creditors billions of dollars every year. I exploit the within-district random assignment of bankruptcy judges to estimate a structural model of bankruptcy. I estimate that liquidation is frequently chosen when a reorganization would have maximized total creditor recovery. Liquidations involving “363 sales,” in which managers sell assets without creditor approval, are especially harmful for creditors. I estimate that courts could dramatically improve creditor recovery by assigning liquidations using a statistical model.

This paper investigates whether risk-related disclosure, which includes aggregate risk disclosure and its tone, including upside and downside risk disclosures, is value relevant for investors in the UK market. Based on 1941 firm-year observations for nonfinancial firms listed on the FTSE All-Share, we employ fixed-effect estimations and find that the value relevance of aggregate risk information is not statistically observable unless a distinction is made in its tone. Specifically, upside- (downside-) risk disclosure significantly increases (decreases) stock prices. We also find that the value relevance of risk information exhibits cross-sectional variations conditional on firms’ growth and profitability. In particular, we find an asymmetric response of stock prices to upside- and downside-risk disclosures for high-growth firms but not those with low growth. In addition, profit-making firms, but not loss-making firms, provide upside and downside disclosures that significantly influence stock prices. Our paper contributes to the extant research on value relevance and risk reporting by providing new evidence on the extent to which, and how, combining the accounting numbers that are examined extensively in prior research with non-accounting information (i.e., risk information) is important for observing value relevance. Our paper also advances prior research on the usefulness of risk disclosure by looking at the tone of this information and how the market responds to each type of disclosure.

This study examines the moderator effect of directors' and officers’ liability insurance (D&O insurance) on the relationship between ownership structure and overinvestment. We find that firms with D&O insurance and higher insurance coverage have a higher probability of overinvestment. These results show the D&O insurance is like a moral hazard effect. We also find evidence that the relationship between ownership structure and overinvestment is affected by D&O insurance. As well as the results support that the firms purchasing D&O insurance is moderator effect on ownership structure and overinvestment.

We carry out a quantitative analysis of the financing measures proposed for the European Deposit Insurance Scheme (EDIS) regarding the target level of the fund and the contribution scheme of member entities. We estimate the loss distribution of the EDIS considering different sources of systemic risk associated with the correlations between bank assets inside and outside the eurozone countries, and we analyse the sensitivity of the results to bank portfolio risk. We determine the variations in the cost of insurance due to the relative risk profile of banks in the Banking Union and the loss-absorbing capacity of the contribution made by countries with the methodologies proposed by the European Banking Authority (2015). Our findings show how the interconnection between banks of different countries has an important influence on accumulated losses in the tail of the distribution, although the correlation within a country exerts a stronger effect. Both aspects must be considered in the calibration of the target level of the fund to avoid underestimating financing needs. Likewise, deterioration in the quality of bank portfolios produces a significant reduction in the fund's loss-absorbing capacity, which calls into question its soundness in times of economic recession. Finally, we assess the impact of the new contribution scheme on moral hazard and the possibility of cross-border subsidies between the countries of the Banking Union. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such “outlier blindness” and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Objective : In this methodologic study (Part 2 of 2), we examined the overlap in sources of evidence and the corresponding results for harms in systematic reviews for gabapentin. Study Design & Setting : We extracted all citations referenced as sources of evidence for harms of gabapentin from 70 systematic reviews, as well as the harms assessed and numerical results. We assessed consistency of harms between pairs of reviews with a high degree of overlap in sources of evidence (>50%) as determined by corrected covered area (CCA). Results : We found 514 reports cited across 70 included reviews. Most reports (244/514, 48%) were not cited in more than one review. Among 18 pairs of reviews, we found reviews had differences in which harms were assessed and their choice to meta-analyze estimates or present descriptive summaries. When a specific harm was meta-analyzed in a pair of reviews, we found similar effect estimates. Conclusion : Differences in harms results across reviews can occur because the choice of harms is driven by reviewer preferences, rather than standardized approaches to selecting harms for assessment. A paradigm shift is needed in the current approach to synthesizing harms.

To help enhancing affordability and availability in the U.S. individual health insurance markets, we evaluate whether expanding interstate markets is associated with efficiency improvement, and the potentials of “Medicaid for All” and “Medicare for All”. This research aims to provide insights and evidence for data-driven decision making in reforming individual health insurance markets and optimizing individual health insurance operations. We employ traditional, non-oriented slack-based, order-α partial frontier, bootstrapped bias-corrected, and modified context-dependent data envelopment analysis (DEA) models, as well as generalized linear, Tobit, and residual inclusion regression models. We find that higher competition or expansion is not associated with higher consumer efficiency or societal efficiency. Our results also indicate that, in minimizing premiums or expenses given enrollment and utilization of medical services, individual health plans are less efficient than Medicaid managed care plans, but more efficient than Medicare Advantage plans. Our findings imply that, for individual plans, expanding interstate markets is not accompanied with lower premiums or expenses without the sacrifice of medical services. This research suggests that it should be advisable to structure individual health insurance markets following the Medicaid managed care model but not the Medicare Advantage model. To “Medicaid-ize” individual markets, we propose to structure the individual coverage in two layers: a conditionally subsidized Medicaid managed care program with mandatory essential benefits, and an unsubsidized “Medicaid Supplement” program for optional additional coverages.

Objectives Primary healthcare will not be effective unless there is a proper referral system. In contrast, comparing the performance of healthcare systems provides an opportunity for policy makers to determine the status of the country’s healthcare system compared with their international counterparts. Therefore, we ranked the countries in terms of indicators affected by the referral system. Methods This study was conducted in 2020. In the first phase, which was to determine the indicators affected by a country’s referral system, data were collected by the Delphi method, and therefore, 13 indicators with a content validity ratio equal to or greater than 0.42 were selected. In the second phase, the data of 13 indicators selected in the first phase were extracted from the 2018 and 2019 World Health Organization reports. The weight of the indicators was calculated based on the Decision-Making Trial and Evaluation Laboratory method–based Analytic Network Process (DANP) and Shannon’s entropy, and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method was used to rank the countries. SPSS 24 and Excel 2013 software were used for data analysis. Results Switzerland, Germany, and Sweden ranked first, second, and third, respectively. In all the 3 countries, there are no mandatory gatekeeping systems. Physicians, especially general practitioners, are the core of primary healthcare, and in all the 3 countries, there is a uniform and coherent health financing system that is either based on mandatory health insurance (Switzerland and Germany) or taxes (Sweden). India had the lowest ranking. Conclusions It seems that the study of the health system of the countries that have obtained higher rankings can indicate their efforts in establishing a gatekeeping system, family physician program, and appropriate financing system. Therefore, other countries can study successful countries and copy them as a model to improve their health system.

Objectives Most spending for prescription drugs is on branded drugs that do not have direct generic equivalents but many of these drugs do have therapeutic alternatives within class. We estimate the potential savings from providing patients a financial incentive to switch from a higher cost drug to a lower cost, therapeutic alternative drug. Methods We used individual state-transition microsimulations to model medication use and spending with and without financial incentives over a 12-month time horizon with a healthcare sector perspective. Costs and utilization inputs were from individuals on branded insulins or multiple sclerosis drugs enrolled in a regional mixed-model health maintenance organization. Base-case model used a one-time financial incentive of $83 and $250 offered to patients on higher cost insulin and multiple sclerosis treatments, respectively, to switch to lower cost drugs within class. Results Savings per individual offered an incentive in the insulin and multiple sclerosis classes were, respectively, $84 (95% CI $46-$122) and $2,127 (95% CI $267-$3,987). Varying the incentive size and switch probability resulted in maximum savings of $712 at elasticity of 0.2 and incentive size $250 for the insulin drug class. For the multiple sclerosis drug class, maximum savings of $5945 was at elasticity of 0.2 and incentive size of $1000. Short time horizon makes our savings estimates conservative. Conclusions If programs such as financial incentives could encourage even a small proportion of patients to switch among drugs within therapeutic class, then substantial savings could be generated.

This paper deals with the optimal reinsurance problem and involves the goals of both insurer and reinsurer. An important novelty may be the incorporation of the background risk that the reinsurer uses in order to diversify (or hedge) the risk ceded by the insurer. Accordingly, general methods to prevent the reinsurer moral hazard must be extended, and a new constraint must be satisfied by the selected reinsurance contract, namely, "the reinsurer increment of risk must be lower than the contract premium". Simultaneously, since the contract must be attractive to the insurer too, "the contract premium must be lower than the insurer risk reduction". Integrating both ideas, "the contract premium must be higher than the reinsurer risk growth and lower than the insurer risk mitigation". Bearing in mind both requirements, that is, the protection against the moral hazard and the spread containing the contract premium, the optimal reinsurance problem is studied under very general conditions about the involved risk measures and premium principles, general solutions are provided, and a practical illustrative example is presented.

The loan evaluation is a fundamental task in peer-to-peer (P2P) lending. Effective loan evaluation can help lenders make informed investment decisions. Existing methods do not consider the return of loans in the core learning stage and thus fail to explore the relationship between the return of loans and their final loan payoff outcomes. In this study, we propose a systematic loan evaluation framework called COst Sensitive Loan Evaluation (COSLE). Specifically, we first develop an instance-aware misclassification cost (IMCO) matrix, which specifies personalized cost for each loan. Then, we present a differential labelling algorithm called DILA cost for assigning node labels and assessing the corresponding cost. By integrating these enhancements into the tree-induction process, we construct a node splitting measurement called COG index. It exploits the relationship between the return information and the final payoff outcome. Additionally, we design the LER evaluation metric to measure the ability of a loan evaluation model to increase the lender’s return. Finally, the COSLE is used to improve popular tree models. Extensive experiments based on the Lending Club dataset show that our COSLE can effectively increase the lender’s return.

New payment models have been introduced by the Centers for Medicare and Medicaid Services to move medicine away from volume-based care toward value-based care. Most models focus on changes for primary care, but specialists like otolaryngologists are wise to familiarize themselves with this changing payment landscape to take advantage of the opportunities and avoid the pitfalls associated with each model.

Background Faced with possible shortages due to COVID-19, many states updated or rapidly developed crisis standards of care (CSCs) and other pandemic preparedness plans (PPPs) for rationing resources, particularly ventilators. Research Question How have US states incorporated the controversial standard of rationing by age and/or life-years into their pandemic preparedness plans? Study Design and Methods This was an investigator-initiated, textual analysis conducted from April to June 2020, querying online resources and in-state contacts to identify PPPs published by each of the 50 states and for Washington, DC. Analysis included the most recent versions of CSC documents and official state PPPs containing triage guidance as of June 2020. Plans were categorized as rationing by (A) short-term survival (≤ 1 year), (B) 1 to 5 expected life-years, (C) total life-years, (D) “fair innings,” that is, specific age cutoffs, or (O) other. The primary measure was any use of age and/or life-years. Plans were further categorized on the basis of whether age/life-years was a primary consideration. Results Thirty-five states promulgated PPPs addressing the rationing of critical care resources. Seven states considered short-term prognosis, seven considered whether a patient had 1 to 5 expected life-years, 13 rationed by total life-years, and one used the fair innings principle. Seven states provided only general ethical considerations. Seventeen of the 21 plans considering age/life-years made it a primary consideration. Several plans borrowed heavily from a few common sources, although use of terminology was inconsistent. Many documents were modified in light of controversy. Interpretation Guidance with respect to rationing by age and/or life-years varied widely. More than one-half of PPPs, many following a few common models, included age/life-years as an explicit rationing criterion; the majority of these made it a primary consideration. Terminology was often vague, and many plans evolved in response to pushback. These findings have ethical implications for the care of older adults and other vulnerable populations during a pandemic.

The time a firm takes to recall products that pose severe hazards has serious implications for the firm and its stakeholders. We examine the role of hazard severity and investigate how it influences the impact of recall experience, type of product defect, and product price on time to recall. Operationalizing time-to-recall as the number of days that elapsed from the time a product was first sold in the market to the date it was recalled, we test our hypotheses using data on 833 toy recalls issued by 445 firms in the U.S. during 1988–2018. We find that, under conditions of high hazard severity, time to recall is longer for (i) firms with past recall experience, (ii) recalls of products involving design defects, and (iii) recalls of high-priced products. We discuss the implications of our findings for research and practice.

The success of behavioral economics has led to a new challenge: many biases offer observationally similar predictions for a targeted financial anomaly. To tame this bias zoo, we combine subjective survey responses with observational data to propose a new approach, one that is robust to question-specific biases introduced through surveys. We illustrate this approach by administering a nationwide survey of Chinese retail investors to elicit their trading motives. In cross-sectional regressions of respondents’ actual turnover on survey-based trading motives, perceived information advantage and gambling preference dominate other motives, though they are not the most prevalent biases based on survey responses.

This review provides a practical guide to the essentials of survival analysis and their reporting in cardiovascular studies, although most of its key content can be extrapolated to other medical fields. This is the first in a series of 2 educational articles laying the groundwork to address the most relevant statistical issues in survival analyses, which will smoothly drive the reader from the most basic analyses to the most complex situations. The focus will be on the type and shape of survival data, and the most common statistical methods, such as nonparametric, parametric and semiparametric models. Their adequacy, interpretation, advantages and disadvantages are illustrated by examples from the field of cardiovascular research. This article ends with a set of recommendations to guide the strategy of survival analyses for a randomized clinical trial and observational studies. Other topics, such as competing risks, multistate models and recurrent-event methods will be addressed in the second article.

In this paper, we investigate an optimal insurance design problem in which the insured's risk preference is of the regret-theoretical expected utility type and admissible insurance contracts are set to satisfy both the principle of indemnity and the incentive-compatible condition. When the insurance premium is calculated by the expected value principle, we show that the optimal solution for a regret-averse insured can be in the form of no insurance or partial insurance above a deductible. We obtain a necessary and sufficient condition for the optimality of no insurance and provide a numerical algorithm to derive the optimal partial insurance above a deductible. Notably, the deductible disappears if and only if the insurance premium is actuarially fair. Finally, we carry out a comparative statics analysis to examine the effect of the insured's regret coefficient on the insured's final wealth and insurance demand. When the insurance pricing is actuarially fair, we find that the increase of the regret coefficient incentivizes the insured to purchase less insurance coverage and leads to an increase in risk of the insured's final wealth. However, our numerical analysis indicates that this finding is not always true for unfair insurance pricing.

Some criticize the data-driven study of driverless car ethics for relying on deterministic crash scenarios where the focus should be on minimizing the occurrence of accidents instead. Indeed, decisions in road traffic are taken in the domain of risk. A relevant ethical question is, however, whether autonomous vehicles should actually minimize an accident’s probability or rather balance the accident’s probability and severity. While ethicists’ introspections might lead them to different conclusions, a participatory paradigm in ethics demands to include laypeople’s intuitions into the societal debate. In three online studies, we found that the mere minimization of accident probability in ethical dilemmas does not correspond to the views of our sample. Our participants favor a balanced consideration of accident probability and severity. This remains true even if the underlying dilemma is based on a more realistic scenario of allocating minor risks among road users. Our findings furthermore illustrate that trolley problems may be informative for autonomous vehicles and emphasize the importance of transparency if manufacturers and the public do not agree on guiding principles in road traffic.

This article is the second of a series of 2 educational articles. In the first article, we described the basic concepts of survival analysis, summarizing the common statistical methods and providing a set of recommendations to guide the strategy of survival analyses in randomized clinical trials and observational studies. Here, we introduce stratified Cox models and frailty models, as well as the immortal time bias arising from a poor assessment of time-dependent variables. To address the issue of multiplicity of outcomes, we provide several modelling strategies to deal with other types of time-to-event data analyses, such as competing risks, multistate models, and recurrent-event methods. This review is illustrated with examples from previous cardiovascular research publications, and each statistical method is discussed alongside its main strengths and limitations. Finally, we provide some general observations about alternative statistical methods with less restrictive assumptions, such as the win ratio method, the restrictive mean survival time, and accelerated failure time model.

This research uses over 750,000 loan records from LendingClub issued from 2015 through 2018 to estimate the gross rates of return (ROR) for each loan by assuming the borrowers stop paying their loans once a default occurs. We find the probability of earning a positive return is 75.6% in terms of the total number of loans. By our calculation, the median of ROR ranges from 4.7% to 10.3% by lending to grade A through D borrowers, but ROR plummets to −6.6% to grade E-and-below borrowers. Regression analysis shows that borrowers' credit rating, loan interest rate, loan status, and paid-month are the most critical factors to influence investors’ ROR. Lastly, we offer a few investment implications and suggestions regarding participation in LendingClub.

This study examines whether sell-side equity analysts engage in “social learning” where their earnings forecasts are influenced by the forecasts and outcomes of other analysts associated with other firms (i.e., the “peers”) in their respective portfolios. We find that analyst optimism is negatively correlated with the recent forecast errors among peers on other firms in the analyst portfolio. An analyst is also more likely to issue “bold” forecasts when similar forecasts were recently issued for other portfolio firms. Analysts learn more from their peers with similar personal characteristics. Overall, social learning is beneficial to analysts and improves their forecast accuracy.

Durable cell and gene therapies potentially transform patient lives, but payers fear unsustainable costs arising from the more than 1000 therapies in the development pipeline. A novel multi-module Markov chain Monte Carlo-based model projects product-indication approvals, treated patients, and product revenues. We estimate a mean 63.5 (54-74 5th to 95th percentile range) cumulative US product-indication approvals through 2030, with a mean 93000 patients treated in 2030 generating a mean US$24.4 billion (US$17.0B-35.0B, US$73.0B extreme) list price product revenues not including ancillary medical costs or cost offsets. Thus, the likely dozens of durable cell and gene therapies developed through 2030 are unlikely to threaten US health system financial sustainability.

Objectives We investigated how health technology assessment (HTA) organizations around the world have handled drug genericization (an allowance for future generic drug entry and subsequent drug price declines) in their guidelines for cost-effectiveness analyses (CEAs). We also analyzed a large sample of published CEAs to examine prevailing practices in the field. Methods We reviewed 43 HTA guidelines to determine whether and how they addressed drug genericization in their CEAs. We also selected a sample of 270 US-based CEAs from the Tufts Medical Center’s CEA Registry, restricting the sample to studies on pharmaceuticals published from 1991 to 2019 and to analyses taking a lifetime time horizon. We determined whether each CEA examined genericization (and if so, whether in base case or sensitivity analyses), and how inclusion of genericization influenced the estimated incremental cost-effectiveness ratios. Results Fourteen (33%) of the 43 HTA guidelines mention genericization for CEAs and 4 (9%) recommend that base case analyses include assumptions about future drug price changes due to genericization. Most published CEAs (95%) do not include assumptions about future generic prices for intervention drugs. Only 2% include such assumptions about comparator drugs. Most studies (72%) conduct sensitivity analyses on drug prices unrelated to genericization. Conclusions The omission of assumptions about genericization means that CEAs may misrepresent the long run opportunity costs for drugs. The field needs clearer guidance for when CEAs should account for genericization, and for the inclusion of other price dynamics that might influence a drug’s cost-effectiveness.

A model of over-the-counter markets is proposed. Some asset buyers are informed in that they can identify high-quality assets. Heterogeneous sellers with private information choose what type of buyers they want to trade with. When the measure of informed buyers is low, there exists a unique and stable equilibrium, and interestingly, price, trading volume and welfare typically decrease with more informed buyers. When the measure of informed buyers is intermediate, multiple equilibria arise, and price, trading volume and welfare may decrease or increase with more informed buyers depending on the equilibrium being played. A switch from one equilibrium to another can lead to large drops in liquidity, price, trading volume and welfare, like a financial crisis. The measure of informed buyers is then endogenized by allowing buyers to invest in a technology that enables them to identify high-quality assets. In this case, the model features endogenous strategic complementarity in acquiring the information technology. Multiple equilibria still exist, with different measures of informed buyers, but a scheme of tax/subsidy on information acquisition sometimes leads to the unique equilibrium.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-19; HCoV-19; COVID-19) has affected all daily activities. Has it also affected the number of United States (FDA) drug approvals over time? The short answer from empirical time series forecasting is not yet. Care should be taken as the crisis continues through maintaining the scientific, economic, political, and social supportive structures to sustain momentum. This conclusion is based on analyzing the results of (non-overlapping) forecasting routines (viz., complex exponential smoothing, auto-regressive fractionally integrated moving average, extreme learning machine, and multi-layer perceptron) performed on longitudinal (1939-present) FDA (CDER) drug approvals taking into regard pre- and extant-COVID-19 eras. This is an initial study and there are caveats with the approach, and as such, all data and programs are provided to support replication of the results and furthering of the investigation. Supplementary information The online version of this article (10.1007/s43441-020-00249-6) contains supplementary material, which is available to authorized users.

This research proposes a dynamic control modeling of income allocation between life insurance purchase and consumption subject to market incompleteness. We adopt a no-good-deal specification of the multiplicity of stochastic discount factors, and treat death as a stopping time on the individual’s health status. Health status non-tradability renders death risk non-hedgeable and the life insurance market incomplete. The no-good-deal condition provides useful guidance for individuals to choose the insurance-income ratio inside good-deal bounds. The magnitude of the influence of market incompleteness on life insurance demand (measured as the width of good-deal bounds) displays life-cycle patterns that converge over time and are highly sensitive to health status characteristics.

Managers and corporate directors need to recognize two key behavioral impediments that obstruct the process of value maximization, one internal to the firm and the other external. I call the first obstruction behavioral costs. Behavioral costs, like agency costs, tend to prevent value creation. Behavioral costs are the costs associated with errors that people make because of cognitive imperfections and emotional influences. The second obstruction stems from behavioral errors on the part of analysts and investors. These errors can create gaps between fundamental values and market prices. When they do, managers may find themselves conflicted, unsure of how to factor the errors of analysts and investors into their own decisions. Proponents of value based management emphasize that with properly designed incentives, managers will maximize the value of the firms for which they work. As such, either they treat behavioral costs as simply another form of agency costs, or they deny the relevance of cognitive errors. In contrast, proponents of behavioral finance argue that behavioral costs are typically large, and cannot be addressed though incentives alone. This is not to say that incentives are immaterial. On the contrary, incentives are of critical importance. The point, however, is that there are limits to incentives. If employees have a distorted view of what is in their own self-interest, or if they have a mistaken view of what actions they need to take in order to maximize their self-interest, then incentive compatibility, although necessary for value maximization, will not be sufficient.

I estimate a structural model of teams, autonomy, and financial performance, using a large cross section of British establishments. My findings suggest that team production does in fact improve financial performance for the typical establishment but that self-managed or autonomous teams do no better than closely supervised or non-autonomous teams. I also find that the magnitude of benefits to establishments that are helped by teams far exceeds the magnitude of costs to establishments that are hurt by teams. Finally, I find two interesting patterns of correlations among the unobserved determinants of teams, autonomy, and financial performance. First, unobserved factors that increase the propensity to engage in teams are positively correlated with unobserved determinants of financial performance. Second, unobserved factors that increase the propensity to grant teams autonomy are negatively correlated with unobserved determinants of financial performance when teams are used.

This article examines the effect upon damages for personal injury of methods used in the United States of America to calculate loss of future earnings. The work of lawyers is examined from the perspective of labour economists. The damages calculated by using these alternative methods are compared with those actually awarded in over a hundred cases determined by courts in England and Wales. This interdisciplinary and comparative study reveals that the tort system fails to satisfy one of its main objectives in that it does not provide recipients of damages with ‘full’ compensation.

Draft lottery data combined with Danish longitudinal administrative records show that military service can reduce criminal activity for youth offenders who enter service at ages 19-22. For this group property crime is reduced for up to five years from the beginning of service, and the effect is therefore not only a result of incapacitation while enrolled. We find no effect of service on violent crimes. We also find no effect of military service on educational attainment and unemployment, but we find negative effects of service on earnings. These results suggest that military service does not upgrade productive human capital directly, but rather impacts criminal activity through other channels, for example by changing the attitudes to criminal activity for this group.

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.

This study considers an item that can be operated over an indefinitely long run operation cycle n (n = 1, 2, 3, …), where each operation period causes a random amount of damage to the item, where the damage is accumulated over time, and where the item fails when the cumulated damage/wear exceeds a failure level ξ0. For such an item, a warranty policy in a discrete-time form is considered. It is suggested that the discrete-type warranty policy that consists of a renewable free-replacement and one-inspection service can increase the probability of warranty success and decrease the costs due to the warranty contract. Under such a warranty policy, with a coverage period of N, items from a mixed population are inspected at an intermediate operation cycle M (

Objectives This study aimed to identify the methods used to determine the value of informal care provided to people living with dementia and to estimate the average hourly unit cost by valuation method. Methods A literature search in MEDLINE Complete, CINAHL, PsycINFO, EconLit, EMBASE and NHS Economic Evaluation Database was undertaken. Following the screening of title, abstract, and full text, characteristics of eligible studies were extracted systematically and analyzed descriptively. The corresponding hourly cost estimates were converted into 2018 US dollars based on purchasing power parities for gross domestic product. Results A total number of 111 articles were included in this review from 3106 post-deduplication records. Three main valuation methodologies were identified: the replacement cost method (n = 50), the opportunity cost approach (n = 36), and the stated preference method based on willingness to pay (n = 3), with 16 studies using multiple methods and 6 studies not specifying the valuation method. The amount of informal care increased as the condition of dementia progressed, which was reflected in the cost of informal care. The average hourly unit cost used to value informal care was US $16.78 (SD = US $12.11). Although the unit cost was approximately US $15 per hour when using the opportunity cost method and US $14 when using the stated preference method, the highest unit cost was obtained when using the replacement cost method (US $18.37, SD = US $13.12). Conclusions Although costs of informal care should be considered when undertaking an economic evaluation or estimating the overall costs of dementia from a policy and priority-setting perspective, further research into applying consistent approaches to valuation is warranted.

Objectives Using a taxonomy previously developed, we assessed the strengths and limitations of available value assessment frameworks (VAF) in Latin America. Methods Systematic review of peer-reviewed journals, gray literature review, and surveys to ISPOR Latin America Industry Committee members were done to identify and select current VAF. Once selected, independent reviewers, organized by pairs, assessed each framework’s input, methodology, and outputs. Results We assessed 7 of 9 VAF in the region, excluding 2 that were not currently in use. The review included 1 framework developed by a regional entity, and 6 country frameworks for either price assessment or to inform reimbursement. Most of these frameworks had a clear definition of the purpose (6 of 7) but could provide more details on the conceptual approach, including perspectives, methods for obtaining preferences, and the ability to incorporate multiple value dimensions (2 of 7). Most lacked information about inclusions/exclusions of elements included in the framework, and whether it assumes a base case comparator and how it is selected. The description of the evaluation of data sources and their scientific validity was inconsistently reported (3 of 7). Few included an assessment of the intervention’s effect on total costs of treating a defined population (2 of 7), or a description of how uncertainty could be incorporated (3 of 7). Finally, potential conflicts of interest among those creating the framework are not sufficiently addressed (0 of 7). Conclusions In the 7 frameworks assessed in Latin America, there are opportunities to improve dimensions, methods, and scope. Addressing these issues will strengthen these VAF for policy and clinical decision making.

In this paper, we analyze a decision-making problem for an insurer with limited liability, who is subject to a solvency constraint and wants to maximize the expected value through reinsurance purchase and external financing. We impose mild conditions on the reinsurance premium principle, which are the axioms of law invariance, risk loading and preserving the convex order. These three axioms are satisfied by all the widely used premium principles, except the Esscher principle, listed in Young (2004) . If value at risk (VaR) or conditional value at risk (CVaR) is adopted to calculate the insurer's regulatory capital, we show that the optimal reinsurance can be in the form of two layers. The optimal reinsurance form is reduced to one layer once the premium principle further satisfies a weak condition. In particular, under Wang's, expected value, variance and Dutch premium principles, we derive the insurer's optimal strategies of reinsurance and financing, which are obtained explicitly under the VaR risk measure but have to be solved numerically for the CVaR risk measure. Results indicate that the insurer has three types of optimal strategies: the reinsurance only strategy, external financing only strategy, and mixed strategy. Moreover, the insurer's attitude toward ceding the risk through reinsurance and increasing capital through external financing is often greatly affected by the regulatory regime, the financing cost and the reinsurance price.

We use field experiments in the employee giving program at Google to demonstrate that hassle costs inhibit charitable donations. Relative to the control condition involving donation by credit card on the charity's website, the treatment group received a simpler option to donate via payroll deduction, thereby decreasing the ‘hassle cost’ of giving by a couple of minutes. In each of two experiments, we found that reducing hassle costs increased the probability of donation to a promoted charity by at least 50%, without reducing average gift size. Results from the two experiments lead us to conclude that the convenience of payroll deduction reduces hassle costs and produces increas in workplace giving for promoted charities. Google later rolled out the program more widely, and suggestive evidence shows that while payroll deduction was popular and increased frequency of donations, it did not increase overall workplace charitable giving.

We provide a characterization of comparative weak risk aversion and comparative RDEU risk aversion for RDEU preferences and, in particular, we correct a claim made by Quiggin (1993) regarding comparative RDEU risk aversion. We then apply the analysis of comparative risk aversion to a problem of optimal design of underwriting contracts in securities issuance. Specifically, in public offerings of equity, an investment banking firm (the underwriter) plays an insurance role: through the underwriting contract, the issuing firm transfers the issue risk to the underwriter, as would an insured to an insurer. We extend a classical model proposed by Mandelker and Raviv (1977) to situations where the issuing firm and the underwriter have RDEU preferences. Assuming that the issuing company’s and the underwriter’s utility functions are concave and linear, respectively, and that either the underwriter is risk neutral or both the issuing company and underwriter are strongly risk averse, we show that a firm-commitment contract is optimal if and only if the issuing company’s probability weighting function dominates the underwriter’s.