Search

Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building-level adaptation and flood insurance, the agent-based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk-based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry-floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk-mitigation investments improves the uptake and affordability of dry-floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.

Technology market has been proposed to benefit technology development and increase productivity, and there is a growing interest on technology market in recent years. However, there is little specific guidance documented the link between patent-based measures and patent collateral. Therefore, the purpose of this article attempts to investigate how patent-based measures and patents used as collateral are related. This article employed eight frequently used patent-based measures categorized into three dimensions, i.e., knowledge flow, technological diversity, and legal protection, to analyze which factors increase patent collateral likelihood and patent collateral speed. The results of this article shows that reference count, times cited count, originality index, generality index, claim count, and family size all increase patent collateral likelihood. On the other hand, reference count, nonpatent reference count, originality index, claim count, and family size accelerate patent collateral speed.

The planning fallacy is at play in projects when optimism bias and/or strategic misrepresentation are present. We examine the cost performance of approximately US$ 6.5 billion worth of social infrastructure projects that were procured in Hong Kong and specifically the differences between their final accounts and the various types of estimates that were prepared prior to construction. We focus on the (honest) planning fallacy and hence aim to determine whether estimates are more optimistic than actual costs. Our data shows that 43% of projects incurred a cost underrun from their contract award. We therefore infer that at best 57% of projects may be explained by the presence of the planning fallacy. Based on our findings, we argue that optimism and pessimism bias coexist. Likewise, the planning fallacy and competing explanations can simultaneously account for cost deviations in social infrastructure projects. We submit that the prevalence of the planning fallacy has been exaggerated and hence provide an explanation as to why this has occurred. We finally suggest there is a need for additional empirical work to test the claim that the planning fallacy is the underlying cause of cost overruns and thus the best “theory” to explain “how projects work.”

To manage the risk of insurance companies, a reinsurance transaction is among the myriad risk management mechanisms the top ranked choice. In this paper, we study the design of optimal reinsurance contracts within a risk measure minimization framework and subject to the Vajda condition. The Vajda condition requires the reinsurer to take an increasing proportion of the loss when it increases and therefore imposes constraints on the indemnity function. The distortion-risk-measure-based objective function is very generic, and allows for, for example, an objective to minimize the risk-adjusted value of the insurer’s liability, and for heterogeneous beliefs regarding the loss distribution by the insurer and reinsurer. Under a mild condition, we propose a backward-forward optimization method that is based on a marginal indemnity function formulation. To show the applicability and simplicity of our strategy, we provide three concrete examples with the Value-at-Risk: one with the risk-adjusted value of the insurer’s liability, one with an objective function that follows from imposing Pareto optimality, and one with heterogeneous beliefs. We conclude this paper with an empirical application with Danish fire insurance losses and the Value-at-Risk and the Tail Value-at-Risk.

Variable annuities, the largest liability of U.S. life insurers, are investment products containing long-dated minimum return guarantees. I show that guarantees with similar economic risks are treated differently by regulation and these differences impact insurers’ hedging behavior. When the regulatory regime recognizes certain risks, insurers start to hedge these risks in a substantial way. For some guarantees, this involves hedging both interest rate and equity market risks. However, for others, it involves hedging only equity market risk. As the regulatory regime still does not recognize the interest rate risk of all guarantees, insurers remain exposed to substantial interest rate risk. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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.

Objectives. To identify the association between Medicaid eligibility expansion and medical debt. Methods. We used difference-in-differences design to compare changes in medical debt for those gaining coverage through Louisiana’s Medicaid expansion with those in nonexpansion states. We matched individuals gaining Medicaid coverage because of Louisiana’s Medicaid expansion (n = 196 556) to credit report data on medical debt and compared them with randomly selected credit reports of those living in Southern nonexpansion state zip codes with high rates of uninsurance (n = 973 674). The study spanned July 2014 through July 2019. Results. One year after Louisiana Medicaid expansion, medical collections briefly rose before declining by 8.1 percentage points (95% confidence interval [CI] = –0.107, –0.055; P ≤ .001), or 13.5%, by the third postexpansion year. Balances also briefly rose before falling by 0.621 log points (95% CI = –0.817, –0.426; P ≤ .001), or 46.3%. Conclusions. Louisiana’s Medicaid expansion was associated with a reduction in the medical debt load for those gaining coverage. These results suggest that future Medicaid eligibility expansions may be associated with similar improvements in the financial well-being of enrollees.

This research advances the hypothesis that female leaders – chief executive officers (CEOs), chairs, and directors – of a microfinance institution (MFI) give more priority to the poorest families in loan provision than male leaders do. We differentiate between a depth and a width dimension of financial inclusion. The data set is a unique global panel of MFIs collected from MFI raters’ reports. Our sample is also unique in the sense that about one-third of all MFIs have a female CEO. The problem of endogeneity for the female leader is resolved by running Heckman’s two-step endogenous dummy variable estimation with an instrument for the female leader. We find evidence of greater depth financial inclusion (smaller average loans, more gender bias) with a female leader but not for width financial inclusion (credit client growth). Female leaders exhibit greater altruism and greater competition avoidance but not greater risk aversion than male peers.

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.

Some consumers lack the cash needed to pay for medical care. As a result, they either delay care until they can pay for it or they forgo the care altogether. To test for such a possibility, we study the distribution of monthly Social Security checks among Medicare Part D enrollees. When Social Security checks are distributed, prescription fills increase by 6-12 percent. In that sense, drug consumption of low-income Medicare recipients is "liquidity sensitive." We then study recipients who transition onto a program that eliminates copayments. When those recipients do not face copayments, their drug consumption becomes less liquidity sensitive. That finding implies that, beyond risk protection, generous insurance also provides recipients with the ability to consume healthcare when they need it rather than when they have cash. Further, we find that recipients whose drug consumption is most liquidity sensitive exhibit price elasticities of demand that are twice the size of the average elasticity, suggesting that more-generous insurance causes recipients both to re-time prescription filling and also to start filling prescriptions that they otherwise would not fill. We present a stylized model that uses this finding to call into question the conventional interpretation of demand-response to price as solely inefficient moral hazard.

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.

International audience; Prevention efforts, such as quitting smoking, flu vaccination, and exercising, are of crucial importance in health policy, but people tend to undertake too few of them. The main reason is that most prevention efforts only reduce but do not completely eliminate the risk of poor health. This makes it harder for people to assess the benefits of prevention, because they tend to misperceive and transform probabilities. In “When Risk Perception Gets in the Way: Probability Weighting and Underprevention,” Baillon et al. introduce psychological insights (probability weighting) in a model of optimal decision making and show that most people undertake too little prevention when the risk of poor health is between 10% and 80%. The paper discusses several policy measures to make people spend more on prevention.

"Network adequacy regulations" expand patients' access to hospitals by mandating a lower bound on the number of hospitals that health insurers must include in their networks. Such regulations, however, compromise insurers' bargaining position with hospitals, which may increase hospital reimbursement rates, and may consequently be passed through to consumers in the form of higher premiums. In this paper, I quantify this effect by developing a model that endogenously captures: (i) how insurers form hospital-networks, (ii) how they bargain with hospitals over rates by threatening to drop them out of network or to replace them with an out of network hospital, and (iii) how they set premiums in an imperfectly competitive market. I estimate this model using detailed data from a Massachusetts health insurance market, and I simulate the effects of a range of regulations. I find that "tighter" regulations, which force insurers to include more than 85% of the hospital-systems in the market, raise the average reimbursement rates paid by some insurers by at least 28%. More moderate regulations, however, can expand the hospital networks without leading to large hikes in reimbursement-rates.

O objetivo deste estudo é apresentar um modelo de previsão de Dificuldades Financeiras (DF) a partir da perspectiva das técnicas de aprendizado de máquina (TAMs). Aplicamos e comparamos os modelos XGBoost, Random Forest e Regressão Logística usando indicadores financeiros para buscar melhores previsões das DFs um ano antes do evento em empresas latino-americanas no período de 2000 a 2017. Nossos resultados mostraram que as TAMs superam o modelo de logit, atingindo uma precisão geral de 96 % (XGboost). Além disso, cinco indicadores foram relevantes para o seu sucesso. O estudo amplia o conhecimento e as discussões ao enfocar o poder preditivo na comparação entre os modelos, destacando os benefícios do uso de algoritmos aplicados à pesquisa financeira. Auxilia na gestão de riscos, na prevenção de perdas, permitindo maior equilíbrio e saúde para o sistema financeiro’, que contribui para o desenvolvimento econômico, social e sustentável de uma sociedade.

Much research on advice taking examines how people revise point estimates given input from others. This work has established that people often egocentrically discount advice. If they were to place more weight on advice, their point estimates would be more accurate. Yet the focus on point estimates and accuracy has resulted in a narrow conception of what it means to heed advice. We distinguish between revisions of point estimates and revisions of attendant probability distributions. Point estimates represent a single best guess; distributions represent the probabilities that people assign to all possible answers. A more complete picture of advice taking is provided by considering revisions of distributions, which reflect changes in both confidence and best guesses. We capture this using a new measure of advice utilization: the influence of advice. We observe that, when input from a high-quality advisor largely agrees with a person’s initial opinion, it engenders little change in one’s point estimate and, hence, little change in accuracy yet significantly increases confidence. This pattern suggests more advice taking than generally suspected. However, it is not necessarily beneficial. Because people are typically overconfident to begin with, receiving advice that agrees with their initial opinion can exacerbate overconfidence. In several experiments, we manipulate advisor quality and measure the extent to which advice agrees with a person’s initial opinion. The results allow us to pinpoint circumstances in which heeding advice is beneficial, improving accuracy or reducing overconfidence, as well as circumstances in which it is harmful, hurting accuracy or exacerbating overconfidence. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

The coronavirus disease (COVID-19) represents a large increase in background risk for individuals. Like the COVID-19 pandemic, extreme events (e.g. financial downturns, natural disasters, and war) have been shown to change attitudes towards risk. Using a risk apportionment approach, we examine whether risk aversion as well as higher order risk attitudes (HORAs) (prudence and temperance) have changed during COVID-19. This methodology allows us to measure model-free HORAs. We include prudence and temperance as higher order measures, as these two have been largely understudied under extreme events but are determinants of decisions related to the health and financial domains. Once we account for socio-demographic characteristics, we find an overall increase in risk aversion during COVID-19. We also find similar results using a hypothetical survey question which measures willingness to take risks. We do not find changes in prudence and temperance using the risk apportionment methodology.

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.

Quality measurement is an intrinsic component of value-based purchasing, yet the quality measures currently in use for Value-Based Purchasing Programs (VBPs) are generally derived from a potpourri of measures originally built for other purposes. Only a handful of VBPs have definitively shown savings or expanded nationally. We suggest that to shift the health care reimbursement system in the United States to paying for value, quality measures used in VBPs should be vetted as "fit for purpose." We advocate that quality measures deemed "fit" for VBPs be defined primarily by the impact of the quality measures on providers, patients, the provider-patient relationship and what matters to patients. We define 5 attributes of quality measures we believe necessary to link the actions of providers and patients and lead to value for our health care system. "Fit for purpose" quality measures should focus health care delivery on value improvement and create a culture of value in our health care system.

The difficulties of the past year have convinced many observers that current risk management practices are deeply flawed, and that such flaws have contributed greatly to the current financial crisis. In this paper, the author challenges this view by showing the need to distinguish between flawed assessments by risk managers and corporate risk-taking decisions that, although resulting in losses, were reasonable at the time they were made. In making this distinction, the paper also identifies a number of different ways that risk management can fail. In addition to choosing the wrong risk metrics and misidentifying or mismeasuring risks, risk managers can fail to communicate their risk assessments and provide effective guidance to top management and boards. And once top management has used that information to help determine the firm's risk appetite and strategy, the risk management function can also fail to monitor risks appropriately and maintain the firm's targeted risk positions. But if risk management has been mistakenly identified as the culprit in many cases, current risk management practice can be improved by taking into account the lessons from financial crises past and present. In particular, such crises have occurred with enough frequency that crisis conditions can be modeled, at least to some extent. And when models reach their limits of usefulness, companies should consider using scenario planning that aims to reveal the implications of crises for their financial health and survival. Instead of relying on past data, scenario planning must use forward-looking economic analysis to evaluate the expected impact of sudden illiquidity and the associated feedback effects that are common in financial crises.

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.

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.

Although studies point to a relationship between debt and crime, there is a limited understanding of their reciprocal relationship and possible mediating risk factors. Moreover, knowledge about the prevalence and scope of debt among offenders is lacking. Therefore, the present study analyzed 250 client files including risk assessment data from the Dutch probation service on the prevalence of debt and possibly related risk factors. The results show that debt is highly prevalent and complex, which underlines the importance of acquiring more knowledge about debt as a potential risk factor for relapse during supervision. It was found that problems with regard to childhood and living situation, education and work/daytime activities, and mental and physical health may be possible underlying risk factors in the relationship between debt and crime. These insights can help professionals adequately support clients with regard to debt in order to prevent recidivism.

How do we combine others’ probability forecasts? Prior research has shown that when advisors provide numeric probability forecasts, people typically average them (i.e., they move closer to the average advisor’s forecast). However, what if the advisors say that an event is “likely” or “probable?” In eight studies (n = 7,334), we find that people are more likely to act as if they “count” verbal probabilities (i.e., they move closer to certainty than any individual advisor’s forecast) than they are to “count” numeric probabilities. For example, when the advisors both say an event is “likely,” participants will say that it is “very likely.” This effect occurs for both probabilities above and below 50%, for hypothetical scenarios and real events, and when presenting the others’ forecasts simultaneously or sequentially. We also show that this combination strategy carries over to subsequent consumer decisions that rely on advisors’ likelihood judgments. We discuss and rule out several candidate mechanisms for our effect. This paper was accepted by Yuval Rottenstreich, decision analysis.

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.

Background Rapid diagnostic tests (RDTs) for malaria are a vital part of global malaria control. Over the past decade, RDT prices have declined, and quality has improved. However, the relationship between price and product quality and their larger implications on the market have yet to be characterized. This analysis used purchase data from the Global Fund together with product quality data from the World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) Malaria RDT Product Testing Programme to understand three unanswered questions: (1) Has the market share by quality of RDTs in the Global Fund’s procurement orders changed over time? (2) What is the relationship between unit price and RDT quality? (3) Has the market for RDTs financed by the Global Fund become more concentrated over time? Methods Data from 10,075 procurement transactions in the Global Fund’s database, which includes year, product, volume, and price, was merged with product quality data from all eight rounds of the WHO-FIND programme, which evaluated 227 unique RDT products. To describe trends in market share by quality level of RDT, descriptive statistics were used to analyse trends in market share from 2009 to 2018. A generalized linear regression model was then applied to characterize the relationship between price and panel detection score (PDS), adjusting for order volume, year purchased, product type, and manufacturer. Third, a Herfindahl–Hirschman Index (HHI) score was calculated to characterize the degree of market concentration. Results Lower-quality RDTs have lost market share between 2009 and 2018, as have the highest-quality RDTs. No statistically significant relationship between price per test and PDS was found when adjusting for order volume, product type, and year of purchase. The HHI was 3,570, indicating a highly concentrated market. Conclusions Advancements in RDT affordability, quality, and access over the past decade risk stagnation if health of the RDT market as a whole is neglected. These results suggest that from 2009 to 2018, this market was highly concentrated and that quality was not a distinguishing feature between RDTs. This information adds to previous reports noting concerns about the long-term sustainability of this market. Further research is needed to understand the causes and implications of these trends.

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.

Much of the research in law and finance reduces long, complex texts down to a small number of variables. Examples include the coding of corporate charters as an entrenchment index or characterizing dense securities complaints by using variables that capture the amount at issue, the statutes alleged to have been violated, and the presence of an SEC investigation. Legal scholars have often voiced concerns that this type of dimensionality reduction loses much of the nuance and detail that is embedded in legal text. This paper assesses this worry by applying text analysis and machine learning to a corpus of more than five thousand complaints filed in private securities class actions that collectively contain over 90 million words. This analysis demonstrates the richness of the information embedded in these complaints by answering three related questions. First, does the text provide indications of the eventual outcomes in the cases? Second, do text-based variables out perform non-text variables in predicting outcomes? And, finally, does text analysis outperform market participants when used as a basis for picking securities? The evidence suggests that the answer to all three of these questions is yes and these findings have several implications. First, the results suggest that there is meaningful information embedded in text and, relatedly, that excessive reduction of dimensions may produce omitted variable bias. Second, evidence of abnormal returns is consistent with the understanding that it takes time for market participants to absorb complex information. Finally, the ability to predict first-filed complaints more readily than consolidated complaints suggests that the goal of the Private Securities Litigation Reform Act to reduce the race to the courthouse and improve the quality of initial complaints has not been fully realized.

The Home Health Value-based Purchasing (HHVBP) demonstration, incorporating a payment formula designed to incentivize both high-quality care and quality improvement, is expected to become a national program after 2022, when the demonstration ends. This study investigated the relationship between costs and several quality dimensions, to inform HHVBP policy. Using Medicare cost reports, OASIS and Home Health Compare data for 7,673 home health agencies nationally, we estimated cost functions with instrumental variables for quality. The estimated net marginal costs varied by composite quality measure, baseline quality, and agency size. For four of the five composite quality measures, the net marginal cost was negative for low-quality agencies, suggesting that quality improvement was cost saving for this agency type. As the magnitude of the net marginal cost is commensurate with the payment incentive planned for HHVBP, it should be considered when designing the incentives for HHVBP, to maximize their 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.

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.

The purposes of clinical decision support include improvement of quality, safety, and cost-effectiveness of care processes. This chapter includes under the term quality measurement (QM) measures of appropriateness of care from all three perspectives of quality: that is, safety and cost-effectiveness are also included. The chapter discusses how measurement and reporting are by themselves a passive form of clinical decision support (CDS). As a first line of using electronic health records (EHRs) to improve care, therefore, there is widespread adoption of efforts to use collected patient data to create quality measures and provide feedback and reporting of them to recipients. Moreover, ratings on quality measures are increasingly used as benchmarks for rating individual providers, practices, or hospitals that are available to the public, or as a metric for value-based reimbursement in emerging accountable care organization (ACO) type models of health care finance reform. Thus, they provide a very strong motivation for the adoption of CDS prospectively to maximize opportunity for meeting benchmark goals. In this chapter, QM (and reporting) are both discussed with respect to being a form of CDS, also showing how they differ from CDS in the traditional sense.

Introduction: In recent decades, the number of professional liability disputes has increased, especially in the civil sector. In these liability assessments, medico-legal experts have become the linchpin. Law No. 24/2017, concerning professional liability in Italy, requires that guidelines and best practices be established to identify the necessary elements for writing expert reports in the legal sector. Materials and Methods: The authors have created a numerical indicator to be applied to 150 legal expert reports on professional liability, enabling them to evaluate the methodological quality of those reports. The results are then compared with the outcome of the legal proceedings in order to establish the suitability of this quality index for evaluating the work of medico-legal experts. Results: Of the 150 reports considered, 14 were scored inadequate, 75 adequate and 31 good. These inadequate (according to the indicator) reports presented a higher probability of being scored inadequate in court proceedings, compared to the risk of inadequacy of all the reports; OR 4.6 (95% CI 1.25-16.90). This probability significantly increased on comparing the inadequate reports with the adequate; OR 5.6 (95% CI 1.28-24.41), and the inadequate with the good; OR 7.73 (95% CI 1.50- 39.87). Discussion: Application of the proposed indicator is simple and produces a high-quality result, thus ensuring an accurate and tenable appraisal of methodological quality. Conclusion: The indicator proposed serves as a useful starting point for creating the necessary methodological standards for medico-legal experts in the field of professional liability as recommended by Law No. 24/2017.

An important societal problem is that people underinsure against risks that are unlikely or occur in the far future, such as natural disasters and long-term care needs. One explanation is that uncertainty about the risk of non-reimbursement induces ambiguity averse and risk prudent decision makers to take out less insurance. We set up an insurance experiment to test this explanation. Consistent with the theoretical predictions, we find that the demand for insurance is lower when the nonperformance risk is ambiguous than when it is known and when decision makers are risk prudent. We cannot attribute the lower take-up of insurance to our measure of ambiguity aversion, probably because ambiguity attitudes are richer than aversion alone.Supplementary Information: The online version contains supplementary material available at 10.1007/s11166-021-09364-7.

In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks.We consolidated the output of three epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds.We report on three key contributions by which our forecasting and reporting system has helped shaping Austria's policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities.Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.During the SARS-CoV-2 pandemic, health authorities make decisions on how and when to implement interventions such as social distancing to avoid overburdening hospitals and other parts of the healthcare system. We combined three mathematical models developed to predict the expected number of confirmed SARS-CoV-2 cases and hospitalizations over the next two weeks. This provides decision-makers and the general public with a combined forecast that is usually more accurate than any of the individual models. Our forecasting system has been used in Austria to decide when to strengthen or ease response measures.

We study primary care physicians' prevention and monitoring technology adoption. Physicians' adoption decisions are based on benefits and costs, which depend on payment incentives, educational assistance, and market characteristics. The empirical study uses national Norwegian register and physician claims data between 2009 and 2014. In 2006, a new annual comprehensive checkup for Type 2 diabetic patients was introduced. A physician collects a fee for each checkup. In 2013, an education assistance program was introduced in two Norwegian counties. We estimate adoption decisions by fixed-effect regressions, and two-part and hazard models. We use a difference-in-difference model to estimate the education program impact. Fixed-effect estimations and separate analyses of physicians who have moved between municipalities support a peer effect. The education program has a strongly positive effect, which is positively associated with a physician's number of diabetic patients, and the fraction of physician-adopters in the same market.

The work proposes a mathematical model of the process of COVID-19 epidemic as it evolved in New Zealand. The model uses a system of differential equations which emanate from natural assumptions on some probability measure and evolution of this measure on evolving family of simplexes. The authors tried to create the model which, at one hand, is simple and easy to follow. and, at the other hand, reflects the observed epidemic process correctly. The practical aim was to come to justifiable estimations of important parameters like the rate of infection as function of time, thus quantifying effectiveness of the Government measures. Another parameters estimated were the probability distribution of detection times and recovery times. [ FROM AUTHOR] Copyright of Journal of Applied Statistics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Poverty is a condition that is below the line of minimum requirement standard values, both for food and non-food. The Government of Indonesia has various programs to overcome poverty-based assistance social, including the family hope program. This family hope program is the provision of conditional cash assistance to very poor households in which there are pregnant women, toddlers, elementary, junior high, high school, elderly, and severe disabilities. The amount of assistance obtained based on the level of family poverty with poverty level parameters is seen from the many categories of very poor households concerned along with the obligation of participants to carry out important commitments in the field of Health and Education. The purpose of this research is the development of a mobile-based poor family monitoring application using the k-means clustering method. Validity test results using sample data 21, it can be concluded that the system can group poor families into 7 clusters with a thoroughness rate of 90.4%. Based on these results, K-Means Clustering can be said to have a high accuracy value for clustering poor families.

The issuance of central bank digital currencies became a real policy issue after the announcement of Facebook’s Libra. Which types of product attributes should central bank digital currencies have to be widely accepted? We answer this question by analyzing consumers’ acceptance of hypothetical payment methods. We used Japanese data from the 2019 Financial Literacy Survey to estimate a model of consumers’ ranking of the frequency of the use of five payment methods via three statistical models. The estimates of three models showed that the respondents to the survey value payment methods with shorter transaction times and mobile payment methods. Based on the estimates of these models, we conducted counterfactual simulations for the introduction of the hypothetical mobile version of noncash payment methods that required a shorter transaction time. We found that these hypothetical products would be the most frequently used payment methods on average; however, respondents with experience of financial troubles and with small financial asset holdings would use them less frequently. The results suggest that if the Bank of Japan wanted to issue a central bank digital currency that would be used almost every day as a replacement for cash, policy tools should be utilized to encourage the use of it by these groups as well.

In October 2015, the Guangdong government of China enacted a so-called unified medical insurance payment for patients residing in Guangdong province, which fundamentally simplifies reimbursement procedures of medical insurance for the involved cross-city in-patients. Using a unique confidential dataset from 2013 to 2018 on hemorrhoid treatments at a renowned hospital in Guangzhou, the provincial capital of Guangdong, and exploiting difference-in-differences estimations based on the abovementioned policy, we document that the physicians' incentives are a negative externality of the full medical insurance policy for cross-city in-patients and account for a 49% probability increase in improper treatments; and neither increasing the communication between physicians and patients nor enhancing the education level of patients reduces the physician-induced demand for improper treatments. A series of robustness tests indicate our findings are solid. In summary, we highlight the substantial roles of medical insurance as a driver of physician-induced demand in an emerging economy such as China.

Tax aggressiveness is the actions taken by the company to reduce its tax obligations. A company is said to carry out tax aggressiveness if the company tries to reduce the tax burden aggressively, either using legal methods, namely tax avoidance or illegal methods such as tax evasion. Although not all tax planning actions are carried out illegally, the more loopholes a company uses to avoid taxes, the more aggressive the company is considered. And this study aims to examine the relationship between the dependent variable and the independent variable of this study. The independent variables are liquidity, ROA, leverage and firm size and the dependent variable is tax aggressiveness. And for the analytical method used is regression analysis, and descriptive analysis. Descriptive statistics are used to describe or describe the variables in the study. Descriptive statistics used are measures of tax aggressiveness of all sample companies. The description of the variables can be seen from the mean and standard deviation. The descriptive statistical test was carried out with the SPSS program. The results of the study found that liquidity has no effect on tax aggressiveness, then Renturn on Assets (ROA) affects tax aggressiveness, then leverage affects tax aggressiveness and company size affects tax aggressiveness. Keywords: Liquidity, ROA, Leverage, Company Size, Tax Aggressiveness

We examine whether the extent of financial statement disaggregation affects the pricing of audit engagements in the United States. We hypothesize and find that auditors assess higher engagement risk and charge higher audit fees for clients with more disaggregated financial statements. We also find that these higher audit fees are not the result of either increased auditor effort to mitigate detection risk or heightened inherent and control risks reflected in lower financial reporting quality. Instead, we document that greater financial statement disaggregation is positively associated with litigation risk. This result suggests that higher audit fees arise out of auditors' assessments of heightened client and auditor business risks associated with litigation. While financial statement disaggregation may have beneficial effects on the quality of financial information, we document that financial statement disaggregation can impose costs on firms in terms of higher audit fees and a higher likelihood of litigation. Our study informs standard setters, firms and auditors as they debate the costs, benefits and extent of disaggregation to be presented in financial statements. Ministry of Education (MOE) Kevin Koh and Yen H. Tong acknowledge funding support fromthe Ministry of Education, Singapore (RG64/19)

ZusammenfassungEinleitung In Deutschland wurde der Rettungsdienst im Jahr 2016/2017 bei insgesamt rund 7,3 Mio. Notfallereignissen aktiv 1. Informationen zu der dabei erfolgenden prähospitalen Versorgung von Patient*innen finden sich in unterschiedlichen Sekundärdatenquellen; jedoch ist die kombinierte Analyse dieser Daten auf Fall-/Einsatz- oder Patient*innen-Ebene selten der Fall. Um dies zu ändern, ist die Erforschung von Merkmalen und Methoden erforderlich, anhand derer Sekundärdaten, welche präklinische Inhalte umfassen, verknüpft werden können.Methode Es werden Rettungsdienst-Einsatzprotokolle aus 5 bayerischen Rettungsdienstbereichen mit Abrechnungsinformationen 10 gesetzlicher Krankenkassen zusammengeführt (Daten aus 2016). Demonstriert werden 2 Verknüpfungsansätze auf Ebene patient*innenindividueller Einsätze bzw. abgerechneter Leistungen. Zuerst erfolgt die Verknüpfung deterministisch anhand des Personenidentifikators Krankenversichertennummer (KVNR). Der zweite Verknüpfungsansatz beruht auf einem probabilistischen Verfahren, das neben der nur in einem Teil der Einsatzprotokolle verfügbaren KVNR indirekte Schlüsselvariablen berücksichtigt. Dabei handelt es sich um die Kassenzugehörigkeit der Patient*innen, ihr Geschlecht und Geburtsjahr sowie die zurückgelegte Wegstrecke des eingesetzten Rettungsmittels. Zur Prüfung sowohl der deterministischen als auch probabilistischen Verknüpfung werden Übereinstimmungsquoten verschiedener, in beiden Datenquellen vorhandener Variablen ermittelt.Ergebnisse Ausgangspunkt für die Datenverknüpfungen stellen 106 371 Rettungsdienst-Einsatzprotokolle (unabhängig von der Krankenkasse) sowie 432 693 bei Krankenkassen abgerechnete Leistungen (unabhängig vom Rettungsdienstanbieter) aus dem Bereich der Notfallrettung dar. Von 5921 Einsatzprotokollen, in denen eine an Inno_RD datenliefernde Krankenkasse dokumentiert ist, können 4327 Einsatzprotolle deterministisch unter Rückgriff auf die KVNR mit den Abrechnungsdaten zusammengeführt werden. Im Rahmen der probabilistischen Verknüpfung lässt sich diese Anzahl auf 5379 Einsatzprotolle steigern. Alle Prüfungen deuten sowohl für den deterministischen als auch den probabilistischen Ansatz auf eine hohe Verknüpfungsqualität hin.Schlussfolgerung Eine personen- und einsatzbezogene Verknüpfung von Einsatzprotokollen des Rettungsdienstes mit Abrechnungsdaten ist möglich. Als Alternative zur deterministischen Verknüpfung anhand der KVNR hat sich in Inno_RD ein probabilistischer Ansatz bewährt, auf dessen Grundlage Einsatzprotokolle auch dann sinnvoll verknüpft werden können, wenn KVNR nicht vorliegen sowie indirekte Schlüsselvariablen im geringen Umfang Nicht-Übereinstimmungen oder Missing-Values aufweisen.

We perform a laboratory test of Pledge-and-Review bargaining, implementing a simplified version of the model analysed in Harstad (2021a) . In theory, this institution should increase contributions to a public good only if there is uncertainty over the value of possible future payoffs. In contrast, we find that Pledge-and-Review increases efficiency in all the settings we investigate, and that the improvement is most persistent in our setting without uncertainty. Our results suggest that the Pledge-and-Review institution may be useful, even without uncertainty, as it allows conditional cooperators to test, risk free, the cooperativeness of their partners.

Grading of Recommendations Assessment, Development, and Evaluation (GRADE) is a methodologically rigorous and transparent framework for grading the certainty of evidence and moving from evidence to recommendations in guidelines. Certainty of evidence can be rated as high, moderate, low, or very low, and some domains may lead to an increase or decrease in the certainty of evidence. Once evidence is summarized, the certainty, balance of desirable and undesirable outcomes, patients' values and preferences, equity, acceptability, and feasibility are taken into consideration in order to give a strong or weak recommendation for or against an intervention. In this review we summarize the GRADE approach using urology-related examples. PATIENT SUMMARY: GRADE is a well-established system for determining how much confidence we can place in research evidence and how guideline panels can make recommendations for patient care and health care policy. Recommendations can be strong or weak for or against a treatment strategy. Guideline developers should consider the certainty of the evidence, the balance of desirable and undesirable outcomes, patients' values and preferences, and resource use.