moral hazard in healthcare essay

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• Published: 18 October 2022

A systematic review of strategies used for controlling consumer moral hazard in health systems

• Zohreh Koohi Rostamkalaee 1 ,
• Mehdi Jafari 1 &
• Hasan Abolghasem Gorji 2  

BMC Health Services Research volume  22 , Article number:  1260 ( 2022 ) Cite this article

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Consumer moral hazard refers to an increase in demand for health services or a decrease in preventive care due to insurance coverage. This phenomenon as one of the most evident forms of moral hazard must be reduced and prevented because of its important role in increasing health costs. This study aimed to determine and analyze the strategies used to control consumer moral hazards in health systems.

In this systematic review. Web of Sciences, PubMed, Scopus, Embase, ProQuest, Iranian databases(Magiran and SID), and Google Scholar engine were searched using search terms related to moral hazard and healthcare utilization without time limitation. Eligible English and Persian studies on consumer moral hazard in health were included, and papers outside the health and in other languages were excluded. Thematic content analysis was used for data analysis.

Content analysis of 68 studies included in the study was presented in the form of two group, six themes, and 11 categories. Two group included “changing behavior at the time of receiving health services” and “changing behavior before needing health services.” The first group included four themes: demand-side cost sharing, health savings accounts, drug price regulation, and rationing of health services. The second approach consisted of two themes Development of incentive insurance programs and community empowerment.

Strategies to control consumer moral hazards focus on changing consumer consumptive and health-related behaviors, which are designed according to the structure of health and financing systems. Since “changing consumptive behavior” strategies are the most commonly used strategies; therefore, it is necessary to strengthen strategies to control health-related behaviors and develop new strategies in future studies. In addition, in the application of existing strategies, the adaptation to the structure of the health and financing system, and the pattern of consumption of health services in society should be considered.

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Maximizing the health of people and populations is one of the main goals of any health system which leads to improving personal, social, and economic well-being [ 1 ]. The efficient function of the health system depends on providing improved health services at a minimum cost [ 2 ]. Evidence shows that health spending is growing faster than economic growth [ 1 , 3 ]. On average, health spending in OECD countries was equivalent to 9% of GDP in 2018 [ 1 ]. The United States spent 18% of its GDP on health care in 2015 [ 4 ]. Moral hazard is one of the most important reasons for increasing health costs [ 5 , 6 ]. Moral hazard is the change in health behavior and consumption of health services because of insurance coverage [ 7 ]. According to the theory of moral hazard, health insurance and third-party payers, by lowering the price of care, encourage the consumer to consume more care than when they consume at the market price [ 8 ]. Insurance coverage leads to the consumption of health services above an efficient level [ 9 ].

This phenomenon is classified in different ways: ex-ante moral hazard and ex-post moral hazard, hidden information and hidden action moral hazard, provider moral hazard, and consumer moral hazard [ 10 , 11 ]. Ex-ante moral hazard occurs before illness and increases a person’s unhealthy behavior; in contrast, ex-post moral hazard occurs after the onset of illness and will increase costs by increasing demand and consuming unnecessary services. In the hidden information, the insurer cannot observe the real condition and severity of the disease to pay the cost according to the real need. In hidden action individuals’ precautionary measures are not visible, and the insured person does not take the necessary precautions to prevent the disease. Provider moral hazard occurs when a provider provides more services to increase its revenue. The provider moral hazard is also known as the provider’s induced demand. Consumer moral hazard means insured people demand more care than uninsured people [ 11 , 12 ]. Additionally, consumer moral hazard deals with the reduction in preventive healthcare behaviors resulting from insurance coverage[ 13 ].

Moral hazard is known as one of the main causes of market failure [ 14 ] that has many adverse consequences, such as the impact on demand elasticity, reduction of welfare, inefficiency in using resources, reduction in technical and allocation efficiency, reduction of benefits of risk pooling, and price increase [ 11 , 15 ]. Moral hazard, as a concern in the health insurance market, requires the application of appropriate policies and interventions to be controlled. In this regard, consumer moral hazard as the most obvious form of moral hazard [ 16 ] has been one of the topics of concern for policymakers and economic experts in recent years [ 8 ]. The aim of this study was to determine and analyze strategies used to control consumer moral hazards in health systems. The results of this study can be used for health insurance planning, health system financing, and health cost reduction.

This study was written as part of a Ph.D thesis entitled “Developing a model to control consumer moral hazard in Iran’s health system” which was designed and performed based on its proposal and was approved by the local ethics committee of the Iran University of Medical Sciences (code: IR.IUMS.REC.1399.1103).

Research questions.

What strategies or interventions are used to control consumers’ moral hazards?

What is the approach of identifying strategies to control moral hazards?

Databases and search strategies

For this systematic review, Web of Sciences, PubMed, Scopus, Embase, ProQuest (Dissertations database), and Iranian databases Magiran (the largest Iranian database in various scientific and specialized fields) and SID (open access database to Iranian Persian and English studies) were searched without time limitation, until the seventh of February 2021. In addition, to complete the search and ensure access to all related articles, the Google Scholar search engine was also searched. On July 21, 2022, the mentioned databases were researched to Identify new publications between February 2021and July 2022. During the new search five studies were added.

Search terms were used for the titles or abstracts of the records included “moral hazard”, “unnecessary use”, “unnecessary utilization”, “non-essential use”, “non-essential utilization”, “overutilization”, “health”, “health system”, “health insurance”, “health care”, “health service”, “health services”, “healthcare”, “medical care”, and “medical service”. In Web of Sciences, Scopus, and ProQuest due to their defined search strategy, in addition to the titles and abstracts, keywords were also searched. Search in any of the databases was performed using the defined search strategy of each database. The complete search strategy is shown in Additional file 1 .

Inclusion and exclusion criteria

Papers in English and Persian languages in the field of reducing and controlling consumer moral hazard in the health system, conducted in a quantitative, qualitative, and mixed methods design with theoretical and empirical approaches, that were of moderate and high quality based on Dixon Wood et al. ‘s checklist [ 17 ] were included. Abstracts, letters to the editors, conference, and seminar presentations were excluded.

Methods of screening and selection criteria

All found articles were imported into Endnote software (version X9 (and duplicate articles were removed. Two researchers who were experts in the research topic and systematic review process independently screened the titles and abstracts of the articles (ZKR and MJ). In the final screening step, the full texts of the remaining articles were independently assessed by two researchers. Disagreements between the two researchers were resolved based on the opinion of a third researcher. Finally, the references of the retrieved articles were reviewed to find related articles that were not found in the first search. The screening process of retrieved papers is presented in Figure 1 . Data extraction was conducted based on author’s name, title, year of publication, country, study design, strategies used to control consumer moral hazards, outcome variables, main results and quality assessment status. The main characteristics of the included study is shown in Additional file 2 .

Literature selection and retrieval flow diagram.

Quality appraisal

In the first stage, the quality of the articles was assessed by two members of the research team who were familiar with the issue of moral hazard (ZKR and MJ), and the consensus was reached regarding the quality of the selected articles. The most important criteria for selecting high-quality articles were their relevance and role in the development of the study concept. Disagreements were resolved by a third author. The next step in the quality appraisal was based on Dixon-Woods et al. ‘s checklist [ 17 ], which included five questions regarding the clarity of the study objective(s), suitability of the study design to the objective(s), presentation of a clear report of the process of generating findings, use of sufficient data to support the interpretations, and use of appropriate analysis methods. The quality of the articles was determined based on 10 scores: 9–10 (high quality), 6–8 (moderate quality), and ≤ 5 (low quality). Articles with a score ≥ 6 were included in the study.

Data analysis

A content analysis approach was used to summarize the findings of this qualitative systematic reviews. In this way, to achieve a general understanding, each article was read and re-read, and then each text was broken into small units called code; then, the codes were classified into categories based on their similarities and differences. After interpreting the categories, based on the purpose of the study, the main themes were identified. The process of coding and classifying the codes were done by two coders) ZKR and MJ).

In the search of databases and other sources, 7488 articles were retrieved, and after removing duplicate sources and applying inclusion and exclusion criteria, 68 eligible articles were selected (Fig. 1 ). General description of the selected studies is shown in Table 1 . As Table 1 shows, out of 68 included papers, the majority of studies were conducted in a period from 2016 to 2021(n = 29), in a quantitative approach (n = 46). And, health service demand and utilization was the most common outcome investigated in these studies. (n = 38).

The content analysis of 68 studies included in the study are presented in the form of two group, six themes, and 11 categories.

Since, based on the moral hazard theory, this phenomenon is defined as a change in consumer behavior because of insurance coverage, focusing on changing behavior and modifying it is the main goal of the controlling strategies. Therefore, the results of this study were summarized in the two groups: “Changing behavior at the time of receiving health services” (Table 2 ) and “Changing behavior before needing health services” (Table 3 ). The first group includes four themes: demand-side cost-sharing or consumer cost-sharing, health savings accounts, drug price regulation, and rationing for health services. The second group includes two themes: development of incentive insurance programs and community empowerment.

The relationship between these two groups is drawn in the form of a diagram (Fig. 2 .)

In reviewing the findings of the review studies, the results of four primary studies [ 18 , 19 , 20 , 21 ] were repeated in one review study [ 22 ] and the results of one primary study [ 23 ] were repeated in another review study [ 24 ]. Since in the qualitative analysis, the criteria for analyzing the findings are different from the quantitative results, repeated findings were not excluded in the qualitative analysis, but in the narrative report of the findings of quantitative studies, only the findings of the primary studies were presented.

Relationship of strategies of “Changing behavior before needing health services” with strategies of “Changing behavior at the time of receiving health services”

Strategies for changing behavior at the time of receiving health services

The themes (strategies) in this group, which are used when consumers refer to the health system and receive health services by increasing awareness of services costs and other indirect financial consequences (waiting), attempt to increase their responsibility to reduce the consumption of unnecessary health services. The themes of this group consider the changing consumptive behavior of health service consumers. This group includes four themes: demand-side cost-sharing or consumer cost-sharing, health savings accounts, drug price regulation, and rationing for health services.

Demand side cost sharing

Demand side cost sharing is a financial tool [ 25 ] and a kind of out-of-pocket payment [ 26 ] that is used to decrease the demand for health services or improve the utility of useful services [ 27 ], these strategies provide incentives to reduce unnecessary demands by paying part of the service cost by consumers [ 11 ]. Deductibles (The amount of health costs that a person must pay before the insurance begins to pay), copayment (paying a fixed amount of the cost of each health service unit), coinsurance (paying a percentage of the cost of each health service unit) [ 28 ], and capping (applying a cap on out-of-pocket payments or insurance claims) are different forms of cost sharing [ 15 ] that their design methods are diverse in insurance systems [ 29 ]. Cost sharing is often used in countries with social health insurance systems [ 30 ]. In the United States, it is also used in private insurance and Medicare and Medicaid systems [ 29 ].

The results of the study showed that “demand side cost sharing“ or “consumer cost sharing” were the main strategies used for controlling consumer moral hazard as addressed by 56 studies [ 5 , 6 , 9 , 10 , 13 , 15 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ].

In our analysis, demand-side cost-sharing was divided into two categories: uniform cost-sharing and differential cost-sharing, which were classified based on the fixed or different rates of out-of-pocket payments in the form of deductible, copayment, and coinsurance.

Uniform cost-sharing

The uniform cost-sharing strategy are also known as traditional cost sharing methods includes strategies (codes) in which the rate of patients’ out-of-pocket payments (deductible, copayment, and coinsurance) is fixed or flat to use each unit of health services for different consumers.

Differential cost-sharing

In differential cost-sharing, deductibles, copayment, and coinsurance are adjusted based on criteria such as income level; health status; consumer choice, and the type, value, price, and elasticity of the health product or service. Differential cost - sharing have been proposed in response to the high sensitivity of low-income people [ 45 , 49 , 60 , 64 , 69 ] and inefficiency [ 64 ] of fixed and low cost - sharing strategies,

Strategies related to this category include income-based cost sharing, variable deductible plans (Shift deductible, variable deductible, different size of deductible. optional deductible, voluntary deductible (VD), high-deductible health plans (HDHPs), higher insurance deductibles), tier cost sharing (higher copay or coinsurance for services with higher price of the product or service(, value-based cost sharing (lower copay or coinsurance for services with higher costs benefits or lower elasticity), differential cost sharing based on disease status and Cost sharing with discount.

The studies included in this theme investigated consequences such as the utilization of health services, health costs, health related behavior, the choice of insurance plan, and the opinion and acceptance of consumers using quantitative, qualitative, review, and theoretical approaches (model-based), the results of quantitative studies are reported in narrative form as follows: modest to high reduction in health services and medications utilization [ 9 , 20 , 21 , 23 , 33 , 38 , 44 , 45 , 47 , 49 , 51 , 55 , 56 , 64 , 69 , 71 ], reduction in health cost [ 21 , 39 , 41 , 42 , 44 , 53 , 65 , 68 ], low or no significant effect on health services utilization [ 6 , 34 , 35 , 40 , 62 , 63 , 65 ], low or no significant effect on health costs [ 5 ], increasing cost contaminate incentive (CCI) [ 57 ], significant correlation with higher preventive behavior [ 13 , 50 ] modest efficiency gain [ 18 ], increasing medication adherence [ 48 ], increasing demand for low price drug [ 61 ], optimal insurance [ 54 ].

The reported negative consequences are as follows: Decreasing the utilization of both necessary and unnecessary care [ 44 , 55 , 71 ], substitution effect from cares with cost-sharing to free care or with lower cost sharing [ 18 , 70 ], and vulnerability of low-income groups [ 49 , 60 , 68 ].

Despite the focus of differential strategies on eliminating the shortcomings of uniform strategies, some studies indicate the ineffectiveness of these strategies in response to the problem of reduced consumption of non-essential services [ 44 ], delayed treatment, and medical debt in the vulnerable group [ 68 ]. Also, the low level of unhealthy behavior in members of HDHP may be due to the individual characteristics of the people who chose the plan not the impact of the plan [ 50 ].

Health savings accounts (HSAs)

HSAs are considered as alternative tools for financing and dealing with future demographic challenges [ 72 ]. This financing theme under the title of health savings accounts [ 73 , 74 ] or medical savings accounts [ 72 , 75 , 76 ], are currently used to decrease moral hazard and cost in four countries around the world [ 72 , 75 ]. In this mechanism, owners of savings accounts should save a certain percentage of their income in these accounts for future health expenses. Funds of these accounts are used to pay for health expenses. Saving accounts increase people’s motivation to take responsibility by providing tax benefits and informed participation in health care decisions based on cost awareness and monitoring of physicians’ decisions [ 73 ].

In the United States and South Africa, they are used in combination with private insurance and is voluntarily. In Singapore and China, health saving accounts are governmental and compulsory which are implemented in combination with social health insurance and social risk-pooling system, respectively [ 72 , 75 ]. This strategy in private health insurance and combination with high deductible health plans (HDHPs) is known as consumer directed health plans or consumer-driven health plans (CDHPs) [ 59 ].

In our study, health savings accounts were divided into two categories: voluntary health savings accounts (HSAs) and compulsory health savings accounts (HSAs), which were classified according to the mandatory and optional nature of the plan and the type of financing system in each country.

Voluntary health savings accounts

In this category, health savings accounts are optional, and in combination with private insurance and high deductible health plans (HDHPs)that is known as consumer directed health plans or consumer-driven health plans (CDHPs)[ 59 ]. In the United States and South Africa, this strategy are used [ 75 ].

Compulsory health savings accounts

Compulsory health savings accounts are governmental and compulsory which are implemented in combination with social health insurance and social risk-pooling system. Medisave (Medical savings accounts) (MSAs) in combination with social health insurance, Tongdao (MSA) in combination social risk-pooling (SRP) and Bankuai: (MSA) separately to finance outpatient services, are related to this category [ 75 ].

Findings related to health savings accounts were extracted from seven studies [ 55 , 59 , 72 , 73 , 74 , 75 , 76 ]. In studies related to this theme, the outcome such as health services utilization, health costs, and health promotion behaviors was assess using quantitative, review, and model-based theoretical approaches. The narrative reports of these outcomes in the quantitative studies are as follows: reduction in health services utilization and cost [ 75 ], useful for future savings [ 75 ] No or less effective in controlling healthcare costs [ 74 , 76 ].

Some experts regard the usefulness of precautionary savings as positive point of these strategies [ 72 ]. In a study that assessed the effect of these strategies for prevention efforts and precautionary savings, it is stated that consumers do not take these two measures at the same time; in case of precautionary savings, preventive action is reduced, and vice versa [ 73 ]. Adverse selection, consequences of inflation, reduction of equity, and restraint of essential consumption are other negative consequences of this strategy [ 74 ].

Drug price regulation

Drug pricing is an influential component of drug access and rational use of drugs. In addition to improving access, consumption management should be considered [ 77 ]. Drug price regulation is the third theme of this group, with three categories, uniform pricing, discriminatory pricing and two part pricing, which are based on drug pricing policies and the fix or different prices for each drug unit. As the findings of this theme were extracted from only one study, the categories related to this theme included one code that could not be combined and summarized further due to dissimilarity.

Uniform pricing

Uniform pricing refers to strategies in which a product is offered at the same price for all market segment regardless of the characteristics of each segments and its ability to pay [ 78 ]. It is one of the traditional methods of pricing in the pharmaceutical industry [ 79 ]. This pricing method, despite the ease of administration, is not able to satisfy all market segments. From the perspective of high-level customers, the suggested price may be low and indicate low desirability, whereas low-income customers may consider the price high and avoid buying it [ 80 ]. Results related to this category were extracted from a study [ 81 ].

Discriminatory pricing

Discriminatory pricing offers different prices for the same drug in different markets or groups [ 78 ]. Price discrimination is caused by the inability of developing countries to provide the medicines they need. Discriminatory pricing involves a segmented market that charges different prices based on each country’s ability to pay [ 77 ]. This category includes different types; however, in this study, only third degree price discrimination was introduced as an intervention to control moral hazard [ 81 ].

Two part pricing

Two-part pricing, another name for two-part tariffs, determines the price of medicine from the combination of uniform price and lump-sum payments [ 81 ] which has recently been proposed instead of uniform pricing for drugs [ 79 ].

Results related to the “drug pricing” theme were extracted from a study [ 81 ]. In this study, three types of pricing mechanisms, including uniform pricing, two-part tariffs and third degree price discrimination were compared in order to control the consumer’s moral hazard, the results showed two-part tariffs were considered a better strategy to address consumer moral hazard [ 81 ].

Rationing of health services

Rationing of health services based on the waiting list (number) and waiting time (period) is one of the demand management strategies for non-emergency and elective health services [ 82 ]. Rationing of health services is the last theme that has one category named “rationing by waiting” which refers to strategies that control the consumer moral hazard by considering the cost of lost time. This strategy is one of the ways to reduce health costs that replaces user payments in countries without this system (national health system) to control costs and reduces unnecessary demand by imposing costs through the queue [ 30 ]. Findings related to the “ Rationing of health services ” were extracted from three studies [ 10 , 30 , 83 ]. These three studies investigated this mechanism using review and model-based theoretical approaches. The results of the theoretical analysis of these strategies regarding optimality [ 30 ] and well-being [ 83 ] outcomes were not associated with positive results.These strategies are not very popular and people tend to pay instead of waiting [ 83 ].

Strategies for changing behavior before needing health services

The themes in this group deal with the strategies that are applied outside the health system, before the need for healthcare services, and through the consciousness of health and positive financial incentives, increase healthy behavior or prevent unhealthy behavior. Changing individual behaviors to reduce high-risk behaviors and improve health-promoting behaviors is the approach of this group. This group includes two themes: development of incentive insurance programs and community empowerment.

Development of incentive insurance programs

The themes of this group focus on the measures of insurance companies and purchasers. This theme refers to strategies aimed at reducing the risk of disease and the need for health services or unhealthy behaviors and consists of two categories: extending preventive care insurance and developing bonus-oriented insurance. Findings related to this theme were extracted from11 studies [ 10 , 15 , 25 , 37 , 43 , 46 , 58 , 84 , 85 , 86 , 87 ].

Extending preventive care insurance

Expanding preventive care insurance refers to strategies that, by developing various types of preventive insurance, sensitize consumers to their health and reduce the need for more health services in the future by preventing the deterioration of their health status. Additionally, these strategies prevent the demand for specialized and expensive services by providing medium insurance plans [ 37 , 84 ]. This category includes the following strategies (code): proposing insurance coverage for preventive care [ 37 ], separating insurance coverage for prevention and treatment [ 37 ], to encourage insureds to use more secondary preventive care [ 84 ] and improving perception of health status through secondary preventive care [ 84 ].

Developing bonus-oriented insurance

These strategies reduce unnecessary consumption by providing incentives to avoid inefficient service. In these strategies, insurance attempts to control consumers’ moral hazard by applying positive financial incentives in the form of premium discounts [ 10 ], or more coverage [ 86 ] in the following year’s contract, in the case of less service consumption or applying preventive effort Bonuses for non-consumption or limited consumption are often used in risk adjustment schemes [ 58 ].

Health service utilization, health costs, risk-reducing behaviors, and choice of expensive health services were among the variables investigated in studies of this them in quantitative, qualitative, and theoretical approaches. The results of these studies can be summarized as follows: more feasibility to incentivize consumers to purchase more secondary preventive care [ 84 ] higher reduction in moral hazard in the copayment with a premium reduction frame than copayment reduction frame [ 46 ] moral hazard reduction in voluntary deductible is expected to be larger in a system with risk-rated premiums than in a system with community-rated premiums [ 85 ].

Community empowerment

Community empowerment is the second and last theme in this group with one category called Community education.The theme of ”community empowerment”, mentioned by only one study [ 33 ].

Community education

This category refers to the development of health-promoting behaviors through community education and increasing people’s awareness of the function of insurance and the consequences of the unnecessary use of health services by using the capacity of civil society [ 33 ]. Despite the fact that only one study had dealt with this issue marginally, due to the importance of the subject, the research team decided to set this code as an independent theme.

The sources from which each code is extracted are provided in Additional file 3 .

Risk of bias consideration

The risk of bias assessment in this study consisted of the following: To reduce publication bias, unpublished papers were searched in the Dissertations database of ProQuest for grey literature, but no related papers were found. In this regard, there is a possibility of language bias due to the limitation of non-English articles in publishing or indexing the results and the focus of this study on Persian and English articles, which is mentioned as a limitation in the limitations section.

The aim of this study was to determine and analyze strategies used to control consumer moral hazards in health systems. A wide range of goals, approaches, and various research designs have been investigated and reported.

Controlling strategies for consumer moral hazard are known as demand-side strategies whose goal are to motivate consumers to reduce unnecessary demand or consumption. In this study, the strategies to control the consumer moral hazard were divided into two groups.The first group aims to control consumer consumptive behavior when receiving health services. The second group focuses on reducing the need for health services by controlling health-related behaviors before needing health services. This classification of controlling strategies was taken from the approach of dividing moral hazards into ex-post and ex-ante moral hazard. Ex-post moral hazard means an increase in demand for health services due to price reduction, which indicates consumer price sensitivity [ 85 ]. Ex-ante moral hazard refers to a reduction in preventive behaviors and an increase in risky behaviors due to insurance coverage [ 88 , 89 ].

The results of this study show a greater frequency of studies related to the strategies of the first group and control of consumptive behavior, In contrast, strategies used for changing health-related behavior are limited which indicates that researchers pay more attention to ex-post moral hazard. Ex-post moral hazard has been widely studied, but evidence of ex-ante moral hazard is very limited [ 88 , 90 ]. The reason for less attention paid to the ex-ante moral hazard modeled by Ehrlich and Becker in 1972 may be criticized as follows: cost is not the only consequence of illness that, if paid by someone else, makes people indifferent to their healthcare [ 90 , 91 ].

Another noteworthy point of this study’s findings is the financial nature of most strategies, including demand-side cost sharing as one of the most effective methods, health savings accounts, and drug pricing. Imposing a cost through the waiting list can also be considered a financial tool. Access restrictions caused by the negative financial incentives is one of the adverse effects of these strategies. A recent study on cost sharing showed a significant relationship between cost sharing and adult mortality in poor countries. The authors believe that this issue should be considered when analyzing the social welfare consequences of cost-sharing [ 92 ]. Also, during the analysis, Michaela et al. stated that medical savings accounts cause inequality and provide little financial protection [ 93 ].

Although bonus insurance is a positive incentive financial tool because the individual receives a reward in the form of a premium discount or more coverage in exchange for reduced or non-consumption, the results of a qualitative study in insured individuals proved these strategies to be less optimistic and justified compared to demand-side cost sharing strategies [ 25 ]. In addition, the community empowerment strategy, despite being a non-financial tool, needs further investigation in future studies due to the limitations of effective studies.

The outcomes analyzed in the included studies are other points of debate in this review. The majority of the outcomes analyzed included demand and utilization of health services [ 5 , 15 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 30 , 33 , 37 , 38 , 39 , 40 , 43 , 44 , 45 , 47 , 48 , 49 , 50 , 51 , 55 , 56 , 58 , 60 , 62 , 63 , 64 , 65 , 68 , 69 , 70 , 71 , 75 , 84 , 86 ] and health services costs and expenditures, optimality and efficiency of strategies [ 5 , 15 , 30 , 31 , 32 , 36 , 37 , 39 , 41 , 42 , 43 , 44 , 53 , 54 , 57 , 59 , 68 , 70 , 73 , 74 , 75 , 76 , 83 , 85 , 87 , 94 ]. Limited studies have addressed other important aspects such as access to low-income people [ 49 , 60 , 68 ], reduction in the consumption of both essential (such as preventive and diagnostic services) and non-essential services [ 24 , 44 , 58 ], and people’s attitudes and acceptance [ 25 , 34 , 52 , 67 ].

The impact of strategies on outcomes such as the utilization of health services and health costs has been different, which seems normal due to the different implementation and management methods and whether the programs are mandatory or optional. However, in this context, the point to consider is to pay attention to the negative consequences, including the higher sensitivity of low-income groups and the shifting financial burden to insureds and an increase in total costs of health costs due to the substitution effect especially in cost-sharing methods. Thesee consequences challenge the achievement of equity in the access and efficiency of the health system. Since moral hazard is one of the factors of the inefficiency of the health system [ 95 ] therefore, in its control, improving efficiency should be the most important goal.

In this regard, the results of this research showed that controlling strategies need further investigation in future study. Due to the focus of most studies on the controlling strategies of consumptive behavior at the point of receiving the service, therefore, the suggestions are as follows. Reviewing existing strategies, especially strategies with negative financial incentives to minimize adverse consequences, paying more attention to current strategies from the perspective of preventing the need for health services and the introduction of new strategies with preferably non-financial approaches that do not limit access. Obviously, in the design of new interventions and revision of existing interventions, important consequences such as access, financial protection, equality, and quality of services provided along with service utilization and service costs should be taken into consideration.

On the other hand, considering that each of these strategies is used in different health systems with different financing mechanisms, so managers in each health system need to adjust strategy to the characteristics of their health system. In addition, considering the nature of behavioral change of strategies, knowing the characteristics of consumers, the pattern and culture of health service consumption and their health-related behaviors is the first step to choosing the most appropriate strategy and adapting it to each Society’s conditions.

Limitations

This study has several limitations. The first, is the restriction of studies in Persian and English languages. No clear boundary between the consumer and provider moral hazard in some articles is another limitation. The researchers separated these two issues by studying the full text of the articles, focusing on the type and setting of service delivery and the role of physicians in providing services. The last limitation was the methodological diversity and heterogeneity of the quantitative studies, which did not allow for quantitative analysis and reporting the effectiveness of the strategies.

Data availability

All data are within the manuscript and additional files.

Abbreviations

Organization for Economic Cooperation and Development.

Gross Domestic Product.

Value Based Insurance Design.

High Deductible Health Plans.

Consumer-Directed Health Plans or Consumer-Driven Health Plans.

Medical Savings Accounts.

Health Savings Accounts.

Health Reimbursement Accounts.

social risk-pooling.

social insurance pool.

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Acknowledgements

This study was part of a PhD thesis in health services management supported by Iran University of Medical Sciences; Grant No. IUMS/SHMIS-1399-3-37-19512, the authors would like to thank all the staff involved in the School of Health Management and Information Sciences and research department of Iran University of medical sciences.

This study was funded and supported by Iran university of medical sciences (IUMS); Grant No. IUMS/SHMIS-1399-3-37-19512. The funding body had no role in the study design, analysis, and interpretation or in the writing of the manuscript.

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Koohi Rostamkalaee, Z., Jafari, M. & Gorji, H.A. A systematic review of strategies used for controlling consumer moral hazard in health systems. BMC Health Serv Res 22 , 1260 (2022). https://doi.org/10.1186/s12913-022-08613-y

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Moral Hazard in Healthcare Insurance

Concept of moral hazard, implication on public health, justification for the detrimental effects of moral hazard.

Health care insurance is an important facet in one’s life because it increases one’s access to health care. However, individuals tend to take advantage of this fact by seeking services even if it is not necessary, all because it is being catered for by an insurance company: Moral Hazard . This behavior results in increased healthcare costs and strained healthcare services. In order to ensure a balance between the costs and services individuals receive, insurance companies have resorted to imposing copayments and deductibles. This paper gives insight into Moral Hazard and its implication on public health.

Moral hazard in health insurance refers to the additional medical care that an individual gets on the basis of higher insurance coverage. Usually, this prevails among individuals with a health insurance cover only; otherwise, these individuals would not pursue this additional care if they did not have an insurance cover. For example, just because one is insured, he or she does not mind spending more or undergoing unnecessary procedures, which he or she would not have undergone without the cover.

In his explanation, Thoma (2013) indicates that moral hazard on its own, without limitations proposed above by the various health economists, would be detrimental to public health because it would promote irresponsibility and unnecessary costs that would overwhelm the insurance companies as well as the healthcare providers. On the contrary, scholars like Nyman (2007) have remained adamant that the current high incidences and prevalence rates of various health conditions and associated high rates of mortality are attributed to the misconception of the term moral hazard . Hence, the concern for increased health costs warrants better and complete models that will aid in accurate estimates of healthcare utilization. Whereas the rationale behind moral hazard is to increase access to healthcare, the general implication of this concept without some form of limitation is detrimental to public health.

Initially, Nyman (2004) presented a two-sided debate on the impact of moral hazard on public health. He presented the typical argument by health economists using the conventional theory that this additional care is not economically valuable because it is worth less in comparison to its costs of production. Whereas this may be true in reference to utilization of healthcare services on the basis of insurance cover, Geyman (2007) advocates for a consumer-driven healthcare characterized by more co-payments and deductibles while imposing some restrictions. A survey by Einav, Finkelstein, Ryan, Schrimpf, and Cullen (2013) using employee-level data indicates that moral hazard has implications on public health due to increased healthcare costs. Einav et al. (2013) seconds Geyman (2007) on the use of high-deductible plans to reduce spending in healthcare. Aron-Dine, Einav, Finkelstein, and Cullen (2015) indicate that moral hazard has great implications on the nonlinear nature of health insurance as individuals respond to the dynamic incentives contained herein. The development of dynamic incentives in healthcare using the concept of cost sharing has led to remarkable declines in healthcare costs because low coinsurance rates are linked to lower expenditure, and the converse is also true.

In agreement with Pauly (2008), moral hazard escalates disparity in healthcare provision. Regardless of the heated debate and need for high deductibles, these are largely imposed on an individual because employer-provided insurance benefits pose a challenge to the implementation of such plans. Dave and Kaestner (2009) mention, moral hazard is associated with avoidance of preventive practice; instead, individuals take up more of the unhealthy behaviors that tend to overwhelm the health care system. Dave and Kaestner (2009) are seconded by Stanciole (2008), who shows a similar behavioral pattern associated with moral hazard. Despite the fact that such effects are realized based on empirical and opinion facts, feasible frameworks to ensure responsible behavior in preventive care and medical utilization are paramount.

Aron-Dine, A., Einav, L., Finkelstein, A., & Cullen, M. (2015). Moral hazard in health insurance: Do dynamic incentives matter? The Review of Economics and Statistics, 97 (4), 725-741.

Dave, D., & Kaestner, R. (2009). Health insurance and ex ante moral hazard: Evidence from Medicare. International Journal of Health Care Finance and Economics, 9 (4), 367.

Einav, L., Finkelstein, A., Ryan, S. P., Schrimpf, P., & Cullen, M. R. (2013). Selection on moral hazard in health insurance. American Economic Review, 103 (1), 178-219.

Geyman, J. P. (2007). Moral hazard and consumer-driven health care: a fundamentally flawed concept. International Journal of Health Services: Planning, administration, evaluation, 37 (2), 333-351.

Nyman, J. A. (2004). Is ‘Moral Hazard’ inefficient? The policy implications of a new theory. Health Affairs, 23 (5), 194-199.

Nyman, J. A. (2007). American health policy: Cracks in the foundation. Journal of Health Politics, Policy, and Law, 32 (5), 759-783.

Pauly, M. V. (2008). Adverse selection and moral hazard: Implications for health insurance markets. In F. A. Sloan & H. Kasper (Eds.) , Incentives and Choice in Health Care (pp. 103-130). Cambridge, MA: MIT Press.

Stanciole, A. E. (2008). Health insurance and lifestyle choices: Identifying Ex ante moral hazard in the US market. The Geneva Papers, 33, 627-644.

Thoma, M. (2013). Explainer: What is “moral hazard”? Web.

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The Moral-Hazard Myth

By Malcolm Gladwell

Tooth decay begins, typically, when debris becomes trapped between the teeth and along the ridges and in the grooves of the molars. The food rots. It becomes colonized with bacteria. The bacteria feeds off sugars in the mouth and forms an acid that begins to eat away at the enamel of the teeth. Slowly, the bacteria works its way through to the dentin, the inner structure, and from there the cavity begins to blossom three-dimensionally, spreading inward and sideways. When the decay reaches the pulp tissue, the blood vessels, and the nerves that serve the tooth, the pain starts—an insistent throbbing. The tooth turns brown. It begins to lose its hard structure, to the point where a dentist can reach into a cavity with a hand instrument and scoop out the decay. At the base of the tooth, the bacteria mineralizes into tartar, which begins to irritate the gums. They become puffy and bright red and start to recede, leaving more and more of the tooth’s root exposed. When the infection works its way down to the bone, the structure holding the tooth in begins to collapse altogether.

Several years ago, two Harvard researchers, Susan Starr Sered and Rushika Fernandopulle, set out to interview people without health-care coverage for a book they were writing, “Uninsured in America.” They talked to as many kinds of people as they could find, collecting stories of untreated depression and struggling single mothers and chronically injured laborers—and the most common complaint they heard was about teeth. Gina, a hairdresser in Idaho, whose husband worked as a freight manager at a chain store, had “a peculiar mannerism of keeping her mouth closed even when speaking.” It turned out that she hadn’t been able to afford dental care for three years, and one of her front teeth was rotting. Daniel, a construction worker, pulled out his bad teeth with pliers. Then, there was Loretta, who worked nights at a university research center in Mississippi, and was missing most of her teeth. “They’ll break off after a while, and then you just grab a hold of them, and they work their way out,” she explained to Sered and Fernandopulle. “It hurts so bad, because the tooth aches. Then it’s a relief just to get it out of there. The hole closes up itself anyway. So it’s so much better.”

People without health insurance have bad teeth because, if you’re paying for everything out of your own pocket, going to the dentist for a checkup seems like a luxury. It isn’t, of course. The loss of teeth makes eating fresh fruits and vegetables difficult, and a diet heavy in soft, processed foods exacerbates more serious health problems, like diabetes. The pain of tooth decay leads many people to use alcohol as a salve. And those struggling to get ahead in the job market quickly find that the unsightliness of bad teeth, and the self-consciousness that results, can become a major barrier. If your teeth are bad, you’re not going to get a job as a receptionist, say, or a cashier. You’re going to be put in the back somewhere, far from the public eye. What Loretta, Gina, and Daniel understand, the two authors tell us, is that bad teeth have come to be seen as a marker of “poor parenting, low educational achievement and slow or faulty intellectual development.” They are an outward marker of caste. “Almost every time we asked interviewees what their first priority would be if the president established universal health coverage tomorrow,” Sered and Fernandopulle write, “the immediate answer was ‘my teeth.’ ”

The U. S. health-care system, according to “Uninsured in America,” has created a group of people who increasingly look different from others and suffer in ways that others do not. The leading cause of personal bankruptcy in the United States is unpaid medical bills. Half of the uninsured owe money to hospitals, and a third are being pursued by collection agencies. Children without health insurance are less likely to receive medical attention for serious injuries, for recurrent ear infections, or for asthma. Lung-cancer patients without insurance are less likely to receive surgery, chemotherapy, or radiation treatment. Heart-attack victims without health insurance are less likely to receive angioplasty. People with pneumonia who don’t have health insurance are less likely to receive X rays or consultations. The death rate in any given year for someone without health insurance is twenty-five per cent higher than for someone with insur-ance. Because the uninsured are sicker than the rest of us, they can’t get better jobs, and because they can’t get better jobs they can’t afford health insurance, and because they can’t afford health insurance they get even sicker. John, the manager of a bar in Idaho, tells Sered and Fernandopulle that as a result of various workplace injuries over the years he takes eight ibuprofen, waits two hours, then takes eight more—and tries to cadge as much prescription pain medication as he can from friends. “There are times when I should’ve gone to the doctor, but I couldn’t afford to go because I don’t have insurance,” he says. “Like when my back messed up, I should’ve gone. If I had insurance, I would’ve went, because I know I could get treatment, but when you can’t afford it you don’t go. Because the harder the hole you get into in terms of bills, then you’ll never get out. So you just say, ‘I can deal with the pain.’ ”

One of the great mysteries of political life in the United States is why Americans are so devoted to their health-care system. Six times in the past century—during the First World War, during the Depression, during the Truman and Johnson Administrations, in the Senate in the nineteen-seventies, and during the Clinton years—efforts have been made to introduce some kind of universal health insurance, and each time the efforts have been rejected. Instead, the United States has opted for a makeshift system of increasing complexity and dysfunction. Americans spend $5,267 per capita on health care every year, almost two and half times the industrialized world’s median of $2,193; the extra spending comes to hundreds of billions of dollars a year. What does that extra spending buy us? Americans have fewer doctors per capita than most Western countries. We go to the doctor less than people in other Western countries. We get admitted to the hospital less frequently than people in other Western countries. We are less satisfied with our health care than our counterparts in other countries. American life expectancy is lower than the Western average. Childhood-immunization rates in the United States are lower than average. Infant-mortality rates are in the nineteenth percentile of industrialized nations. Doctors here perform more high-end medical procedures, such as coronary angioplasties, than in other countries, but most of the wealthier Western countries have more CT scanners than the United States does, and Switzerland, Japan, Austria, and Finland all have more MRI machines per capita. Nor is our system more efficient. The United States spends more than a thousand dollars per capita per year—or close to four hundred billion dollars—on health-care-related paperwork and administration, whereas Canada, for example, spends only about three hundred dollars per capita. And, of course, every other country in the industrialized world insures all its citizens; despite those extra hundreds of billions of dollars we spend each year, we leave forty-five million people without any insurance. A country that displays an almost ruthless commitment to efficiency and performance in every aspect of its economy—a country that switched to Japanese cars the moment they were more reliable, and to Chinese T-shirts the moment they were five cents cheaper—has loyally stuck with a health-care system that leaves its citizenry pulling out their teeth with pliers.

America’s health-care mess is, in part, simply an accident of history. The fact that there have been six attempts at universal health coverage in the last century suggests that there has long been support for the idea. But politics has always got in the way. In both Europe and the United States, for example, the push for health insurance was led, in large part, by organized labor. But in Europe the unions worked through the political system, fighting for coverage for all citizens. From the start, health insurance in Europe was public and universal, and that created powerful political support for any attempt to expand benefits. In the United States, by contrast, the unions worked through the collective-bargaining system and, as a result, could win health benefits only for their own members. Health insurance here has always been private and selective, and every attempt to expand benefits has resulted in a paralyzing political battle over who would be added to insurance rolls and who ought to pay for those additions.

Policy is driven by more than politics, however. It is equally driven by ideas, and in the past few decades a particular idea has taken hold among prominent American economists which has also been a powerful impediment to the expansion of health insurance. The idea is known as “moral hazard.” Health economists in other Western nations do not share this obsession. Nor do most Americans. But moral hazard has profoundly shaped the way think tanks formulate policy and the way experts argue and the way health insurers structure their plans and the way legislation and regulations have been written. The health-care mess isn’t merely the unintentional result of political dysfunction, in other words. It is also the deliberate consequence of the way in which American policymakers have come to think about insurance.

“Moral hazard” is the term economists use to describe the fact that insurance can change the behavior of the person being insured. If your office gives you and your co-workers all the free Pepsi you want—if your employer, in effect, offers universal Pepsi insurance—you’ll drink more Pepsi than you would have otherwise. If you have a no-deductible fire-insurance policy, you may be a little less diligent in clearing the brush away from your house. The savings-and-loan crisis of the nineteen-eighties was created, in large part, by the fact that the federal government insured savings deposits of up to a hundred thousand dollars, and so the newly deregulated S. & L.s made far riskier investments than they would have otherwise. Insurance can have the paradoxical effect of producing risky and wasteful behavior. Economists spend a great deal of time thinking about such moral hazard for good reason. Insurance is an attempt to make human life safer and more secure. But, if those efforts can backfire and produce riskier behavior, providing insurance becomes a much more complicated and problematic endeavor.

In 1968, the economist Mark Pauly argued that moral hazard played an enormous role in medicine, and, as John Nyman writes in his book “The Theory of the Demand for Health Insurance,” Pauly’s paper has become the “single most influential article in the health economics literature.” Nyman, an economist at the University of Minnesota, says that the fear of moral hazard lies behind the thicket of co-payments and deductibles and utilization reviews which characterizes the American health-insurance system. Fear of moral hazard, Nyman writes, also explains “the general lack of enthusiasm by U.S. health economists for the expansion of health insurance coverage (for example, national health insurance or expanded Medicare benefits) in the U.S.”

What Nyman is saying is that when your insurance company requires that you make a twenty-dollar co-payment for a visit to the doctor, or when your plan includes an annual five-hundred-dollar or thousand-dollar deductible, it’s not simply an attempt to get you to pick up a larger share of your health costs. It is an attempt to make your use of the health-care system more efficient. Making you responsible for a share of the costs, the argument runs, will reduce moral hazard: you’ll no longer grab one of those free Pepsis when you aren’t really thirsty. That’s also why Nyman says that the notion of moral hazard is behind the “lack of enthusiasm” for expansion of health insurance. If you think of insurance as producing wasteful consumption of medical services, then the fact that there are forty-five million Americans without health insurance is no longer an immediate cause for alarm. After all, it’s not as if the uninsured never go to the doctor. They spend, on average, $934 a year on medical care. A moral-hazard theorist would say that they go to the doctor when they really have to. Those of us with private insurance, by contrast, consume $2,347 worth of health care a year. If a lot of that extra $1,413 is waste, then maybe the uninsured person is the truly efficient consumer of health care.

The moral-hazard argument makes sense, however, only if we consume health care in the same way that we consume other consumer goods, and to economists like Nyman this assumption is plainly absurd. We go to the doctor grudgingly, only because we’re sick. “Moral hazard is overblown,” the Princeton economist Uwe Reinhardt says. “You always hear that the demand for health care is unlimited. This is just not true. People who are very well insured, who are very rich, do you see them check into the hospital because it’s free? Do people really like to go to the doctor? Do they check into the hospital instead of playing golf?”

For that matter, when you have to pay for your own health care, does your consumption really become more efficient? In the late nineteen-seventies, the rand Corporation did an extensive study on the question, randomly assigning families to health plans with co-payment levels at zero per cent, twenty-five per cent, fifty per cent, or ninety-five per cent, up to six thousand dollars. As you might expect, the more that people were asked to chip in for their health care the less care they used. The problem was that they cut back equally on both frivolous care and useful care. Poor people in the high-deductible group with hypertension, for instance, didn’t do nearly as good a job of controlling their blood pressure as those in other groups, resulting in a ten-per-cent increase in the likelihood of death. As a recent Commonwealth Fund study concluded, cost sharing is “a blunt instrument.” Of course it is: how should the average consumer be expected to know beforehand what care is frivolous and what care is useful? I just went to the dermatologist to get moles checked for skin cancer. If I had had to pay a hundred per cent, or even fifty per cent, of the cost of the visit, I might not have gone. Would that have been a wise decision? I have no idea. But if one of those moles really is cancerous, that simple, inexpensive visit could save the health-care system tens of thousands of dollars (not to mention saving me a great deal of heartbreak). The focus on moral hazard suggests that the changes we make in our behavior when we have insurance are nearly always wasteful. Yet, when it comes to health care, many of the things we do only because we have insurance—like getting our moles checked, or getting our teeth cleaned regularly, or getting a mammogram or engaging in other routine preventive care—are anything but wasteful and inefficient. In fact, they are behaviors that could end up saving the health-care system a good deal of money.

Sered and Fernandopulle tell the story of Steve, a factory worker from northern Idaho, with a “grotesquelooking left hand—what looks like a bone sticks out the side.” When he was younger, he broke his hand. “The doctor wanted to operate on it,” he recalls. “And because I didn’t have insurance, well, I was like ‘I ain’t gonna have it operated on.’ The doctor said, ‘Well, I can wrap it for you with an Ace bandage.’ I said, ‘Ahh, let’s do that, then.’ ” Steve uses less health care than he would if he had insurance, but that’s not because he has defeated the scourge of moral hazard. It’s because instead of getting a broken bone fixed he put a bandage on it.

At the center of the Bush Administration’s plan to address the health-insurance mess are Health Savings Accounts, and Health Savings Accounts are exactly what you would come up with if you were concerned, above all else, with minimizing moral hazard. The logic behind them was laid out in the 2004 Economic Report of the President. Americans, the report argues, have too much health insurance: typical plans cover things that they shouldn’t, creating the problem of overconsumption. Several paragraphs are then devoted to explaining the theory of moral hazard. The report turns to the subject of the uninsured, concluding that they fall into several groups. Some are foreigners who may be covered by their countries of origin. Some are people who could be covered by Medicaid but aren’t or aren’t admitting that they are. Finally, a large number “remain uninsured as a matter of choice.” The report continues, “Researchers believe that as many as one-quarter of those without health insurance had coverage available through an employer but declined the coverage. . . . Still others may remain uninsured because they are young and healthy and do not see the need for insurance.” In other words, those with health insurance are overinsured and their behavior is distorted by moral hazard. Those without health insurance use their own money to make decisions about insurance based on an assessment of their needs. The insured are wasteful. The uninsured are prudent. So what’s the solution? Make the insured a little bit more like the uninsured.

Under the Health Savings Accounts system, consumers are asked to pay for routine health care with their own money—several thousand dollars of which can be put into a tax-free account. To handle their catastrophic expenses, they then purchase a basic health-insurance package with, say, a thousand-dollar annual deductible. As President Bush explained recently, “Health Savings Accounts all aim at empowering people to make decisions for themselves, owning their own health-care plan, and at the same time bringing some demand control into the cost of health care.”

The country described in the President’s report is a very different place from the country described in “Uninsured in America.” Sered and Fernandopulle look at the billions we spend on medical care and wonder why Americans have so little insurance. The President’s report considers the same situation and worries that we have too much. Sered and Fernandopulle see the lack of insurance as a problem of poverty; a third of the uninsured, after all, have incomes below the federal poverty line. In the section on the uninsured in the President’s report, the word “poverty” is never used. In the Administration’s view, people are offered insurance but “decline the coverage” as “a matter of choice.” The uninsured in Sered and Fernandopulle’s book decline coverage, but only because they can’t afford it. Gina, for instance, works for a beauty salon that offers her a bare-bones health-insurance plan with a thousand-dollar deductible for two hundred dollars a month. What’s her total income? Nine hundred dollars a month. She could “choose” to accept health insurance, but only if she chose to stop buying food or paying the rent.

The biggest difference between the two accounts, though, has to do with how each views the function of insurance. Gina, Steve, and Loretta are ill, and need insurance to cover the costs of getting better. In their eyes, insurance is meant to help equalize financial risk between the healthy and the sick. In the insurance business, this model of coverage is known as “social insurance,” and historically it was the way health coverage was conceived. If you were sixty and had heart disease and diabetes, you didn’t pay substantially more for coverage than a perfectly healthy twenty-five-year-old. Under social insurance, the twenty-five-year-old agrees to pay thousands of dollars in premiums even though he didn’t go to the doctor at all in the previous year, because he wants to make sure that someone else will subsidize his health care if he ever comes down with heart disease or diabetes. Canada and Germany and Japan and all the other industrialized nations with universal health care follow the social-insurance model. Medicare, too, is based on the social-insurance model, and, when Americans with Medicare report themselves to be happier with virtually every aspect of their insurance coverage than people with private insurance (as they do, repeatedly and overwhelmingly), they are referring to the social aspect of their insurance. They aren’t getting better care. But they are getting something just as valuable: the security of being insulated against the financial shock of serious illness.

There is another way to organize insurance, however, and that is to make it actuarial. Car insurance, for instance, is actuarial. How much you pay is in large part a function of your individual situation and history: someone who drives a sports car and has received twenty speeding tickets in the past two years pays a much higher annual premium than a soccer mom with a minivan. In recent years, the private insurance industry in the United States has been moving toward the actuarial model, with profound consequences. The triumph of the actuarial model over the social-insurance model is the reason that companies unlucky enough to employ older, high-cost employees—like United Airlines—have run into such financial difficulty. It’s the reason that automakers are increasingly moving their operations to Canada. It’s the reason that small businesses that have one or two employees with serious illnesses suddenly face unmanageably high health-insurance premiums, and it’s the reason that, in many states, people suffering from a potentially high-cost medical condition can’t get anyone to insure them at all.

Health Savings Accounts represent the final, irrevocable step in the actuarial direction. If you are preoccupied with moral hazard, then you want people to pay for care with their own money, and, when you do that, the sick inevitably end up paying more than the healthy. And when you make people choose an insurance plan that fits their individual needs, those with significant medical problems will choose expensive health plans that cover lots of things, while those with few health problems will choose cheaper, bare-bones plans. The more expensive the comprehensive plans become, and the less expensive the bare-bones plans become, the more the very sick will cluster together at one end of the insurance spectrum, and the more the well will cluster together at the low-cost end. The days when the healthy twenty-five-year-old subsidizes the sixty-year-old with heart disease or diabetes are coming to an end. “The main effect of putting more of it on the consumer is to reduce the social redistributive element of insurance,” the Stanford economist Victor Fuchs says. Health Savings Accounts are not a variant of universal health care. In their governing assumptions, they are the antithesis of universal health care.

The issue about what to do with the health-care system is sometimes presented as a technical argument about the merits of one kind of coverage over another or as an ideological argument about socialized versus private medicine. It is, instead, about a few very simple questions. Do you think that this kind of redistribution of risk is a good idea? Do you think that people whose genes predispose them to depression or cancer, or whose poverty complicates asthma or diabetes, or who get hit by a drunk driver, or who have to keep their mouths closed because their teeth are rotting ought to bear a greater share of the costs of their health care than those of us who are lucky enough to escape such misfortunes? In the rest of the industrialized world, it is assumed that the more equally and widely the burdens of illness are shared, the better off the population as a whole is likely to be. The reason the United States has forty-five million people without coverage is that its health-care policy is in the hands of people who disagree, and who regard health insurance not as the solution but as the problem. ♦

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Demand-side Interventions to Control Moral Hazard in Health Systems, Beneficial or Detrimental: A Systematic Review Study

Zohreh koohi rostamkalaee.

1 Department of Health Services Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Mehdi Jafari

Hasan abolghasem gorji.

2 Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran

Background: Moral hazard is one of the main reasons for health market failure where supply-side and demand-side interventions are used for its control and prevention. This study aimed to identify the effects of demand-side interventions on moral hazards in health systems.

Methods: For this systematic review, electronic databases, including Scopus, PubMed, Web of Science, Embase, ProQuest, Google Scholar’s search engine, and Iranian databases such as SID and Magiran, were investigated. No time limitation was considered in the search process. The narrative synthesis approach was used for data analysis.

Results: Out of 7484 retrieved papers, 61 papers were included in the study. The Identified effects were divided into 2 categories: health services consumption effects and financial effects, which were summarized in the form of advantages and disadvantages. The most important advantages included a decrease in the utilization of different services and a reduction in health expenditures. Also, the most important disadvantages included lower quality of care, shifting financing burden to the consumers, and limited access to necessary care.

Conclusion: The results showed that the most benefits of interventions, especially in cost-sharing and waiting list interventions, are for insurance organizations, where the disadvantages also affect consumers more. Therefore, it is necessary to pay more attention to these effects and their management because a lack of attention in this regard may impair the performance of insurance financial protection and health provision as one of the major goals of the health system.

Introduction

↑What is “already known” in this topic:

Moral hazard is one of the health market concerns that affect both the provider and the consumer of health services, and its control methods are classified into supply-side and demand-side interventions.

→What this article adds:

The effects of demand-side interventions are presented in this work as 2 general effects: health-care consumption effects and financial effects, which are summarized as advantages and disadvantages for each intervention.

The uncertainty feature of health care makes the time of need for health services and their costs unpredictable ( 1 ). Insurance coverage is a solution to the uncertainty of health care( 2 ) and fair financing of health services( 3 ). Experts believe that insurance coverage distorts the patients’ choices and creates a problem known as a moral hazard( 4 ). Moral hazardis a situation in which the consumer demands additional healthservices because of the insurance coverageand reduction in the price of health care ( 5 ). Moral hazardas a topic in the fie ld of behavioral economics in addition to changes in consumption behavior—leads to a reduction in preventive behaviors because of the reduced financial cost of health consequences. Insurance coverage also changes the behavior of the provider so that the provider also has no incentive to provide the optimal service and creates an induced demand for the patient to increase her income and benefits;( 6 ) therefore, it is known as the consumer moral hazardand providermoral hazard( 7 ).

Moral hazard is one of the main reasons for the failure of the healthmarket( 8 ). Reduction in welfare, reduction in insurance coverage, and increase in health costs are the negative consequences of moral hazard ( 9 ). Evidence shows that health care costs have increased in recent years( 10 , 11 ). The increase in health spending was equivalent to 9% of Gross Domestic Product (GDP) in the Organization for Economic Co-operation and Development countries in 2018 and 18% of GDP in the United States in 2015. Moral hazard is recognized as one of the main factors in increasing the cost of health( 12 ).

Some interventions have been proposed to manage and reduce moral hazards. These interventions are divided into 2 categories: ( 1 ) supply-side interventions and ( 2 ) demand-side interventions. Supply-side interventions are used to control provider moral hazards, and demand-side interventions are used to control consumer moral hazards ( 7 ).Referral system and gate-keeping, managed care, payment systems such as diagnosis-related group per capita payment and global payment, consumption pattern review, statistical reports, and prospective consumption monitoring are the most common interventions to prevent and control moral hazards in supply-side ( 13 , 14 ). The most important demand-side interventions include cost-sharing, medical savings accounts (MSA)or health savings accounts (HSA), waiting lists, and nonuse incentives schemes( 7 ).

Cost-sharing is a method whose aim is to increase the responsibility of individuals by participating in the payment of health costs through out-of-pocket payments( 15 ). Cost-sharing is determined in different ways, such as deductibles, coinsurance, copayment, and ceiling ( 16 ). Cost-sharing, while reducing the consumption of health services, can reduce insurance costs by preventing moral hazards. This method is common in countries with social health insurance( 17 ). Medical savings accounts are kinds of personal accounts in which enrollees save a portion of their income to pay for health expenses. Health saving accounts are a financing tool, which is also used to control the consumer moral hazard ( 18 ). The waiting list is a method that rations health care according to the waiting time ( 19 ). The waiting list, by imposing the cost of time instead of paying directly, will reduce moral hazard( 13 ). Nonuse incentive schemes encourage low consumption or nonconsumption in exchange for a lower premium ( 13 )or generous coverage in the next contract ( 20 ). The premium reduction is often used to risk adjustment schemes ( 21 ).

Since the implementation of any intervention requires the identification of possible consequences for planning to be dealt with, this study aimed to identifythe effects of demand-side interventions to control the moral hazard. Our focus in this study is on studies that have sought to reduce consumer moral hazard and used demand-side interventions in this regard. The results of this study are expected to be useful in reducing moral hazards planning and ultimately reducing health costs.

Data Sources and Searches Strategy

In this systematic review, the following electronic databases were searched until February 7, 2021: Scopus, PubMed, ISI Web of Science, Embase, ProQuest, and Iranian databases including SID and Magiran. Google Scholar’s search engine was used to ensure that all relevant records were covered. No time limitation was considered in the search process. On January 15, 2022, the databases indicated were searched to ensure that the most recent relatedstudies were not missed. During the new search, several studies were added. The main keywords used for searching databases included “moral hazard”, “principal agency problem”, “principal-agent dilemma”, “principal-agent problem”, “unnecessary use”, “unnecessary utilization”, “non-essential use”, “non-essential utilization”, “overutilization”, “health”, “health system”, “health insurance”, “health care”, “healthcare”, “health service”, “medical care”, and “medical service” ( Appendix 1 ).

Inclusion and Exclusion Criteria

All Persian and English papers that examined the effect of demand-side interventions on controlling moral hazard or consumer moral hazard in health systems were included in this study. Papers without full texts, letters to editors, books, reports, seminars, and conference presentations were excluded.

Screening and Study Selection

Founded records were imported to the Endnote software Version 9. After removing duplicate papers, 2 skilled researchers independently conducted an initial screening of the records’ titles. In the second step, theabstracts of the remaining papers were screened independently by 2 researchers, and unrelated papers were removed. In the final screening round, the full texts of papers were independently assessed for inclusion and exclusion criteria by 2 authors. Any disagreement between the researchers was resolved by consultation with a third reviewer. Also, references of the selected papers were assessed to find additional papers. The literature selection and retrieval flow diagram are shown in Figure 1 .

Literature selection and retrieval flow diagram

Data Extraction and Quality Assessment

Data extraction was performed based on the following information: author (s), year of publication, country,study language, title, study design, demand-side intervention, analyzed outcome, main results, and quality appraisal score. Quality appraisal of the papers was performed using the Dixon-Woods quality appraisal checklist ( 22 ). The general characteristics of the included studies are presented in Appendix 2 .

Data Analysis

The narrative synthesis approach was used to summarize the results of the studies because the studies were heterogeneous in terms of the type of study, lent of study, type of demand-side interventions and how to implement them, outcome variables, and high diversity in the approach of analyzing and reporting results. Thus, it was not possible to select a common criterion for the relationship between studies for meta-analysis. Hence, the findings are synthesized in text and table format to provide a summary of the effects and consequences of demand-side interventions.

In the search of databases (N = 7468) and other sources (N = 16), a total of 7484 records were found after removing duplicate records and reviewing the inclusion and exclusion criteria during the screening steps of titles, abstracts, and reviewing the full text of selected papers. A total of 61 papers were included in the study ( Fig. 1 ).

The time of publication of the articles are from 1995 to 2000 (N = 1), from 2001 to 2005 (N = 9), from 2006 to 2010 (N = 10), from 2011 to 2015 (N = 17), as well as 24 studies from 2016 to 2021. Most studies were conducted in a quantitative approach (N = 44), but they were also in a qualitative approach (N = 2), review approach (N = 4), and theoretical approach based on the model formulation (N = 11). Demand-side interventions in these studies included cost-sharing (N = 47), medical or health savings accounts (N = 4), waiting lists (N = 4), and nonuse incentives (N = 6).

The main findings of the study on the effects of demand-side interventions were presented in Table 1 . The identified effects of the study were divided into 2 general effects: ( 1 ) health services consumption effects and ( 2 ) financial effectsfor each intervention. Health services consumption effects show the effect of interventions on outcomes such as demand and utilization of various health services, access to health services, and issues related to the quality of health services. Financial effects also show the effect of interventions on the expenditure of different health services, financial effects for consumers, and insurance organizations or other third-party payers.

* High deductible health plans (HDHPS), **consumer-directed health plans (CDHPs), ***Value-based Insurance Design (VBID, ****GPs: general practitioners

Table 2 shows the most important advantages and disadvantages of demand-side interventions.

This systematic review study aimed to identifythe effects of demand-side interventions to control moral hazards in health systems. A variety of study objectives and methods were reviewed and reported in this study. The majority of studies investigated the effects of cost-sharing methods. The basis of cost-sharing goes back to the theory of moral hazards where nonparticipation in costs leads to reckless choices and increased costs ( 76 ). The study’s findings show that a variety of cost-sharing schemes exist—including uniform and fixed-rate cost-sharing, shift deductibles, high-level cost-sharing plans such as higher deductibles, high deductible health plans (HDHPS), consumer-directed health plans (CDHPS), and value-based cost-sharing or value-based insurance design (VBID)—which determines the cost-sharing rate based on the price elasticity of demand for health services. The bulk of the results related to cost-sharing showed a significant reduction in the consumption of health services; a few studies indicated no or little effect on consumption; this variation in results is expected in different studies due to the variety of regulatory cost-sharing rates in different countries. Regarding the reduction of service consumption, some essential points should be mentioned. The transitory effect is one of the significant issues in reducing the consumption of health services. In Kan and Suzuki’s study, the effect of reduction in demand for physician visits following the increase in coinsurance rate was not sustainable 6 months after the implementation of the program ( 36 ).The substitution effect is another significant effect of reducing service consumption. This effect shifts services with cost-sharing to free services or services with less cost-sharing ( 43 , 27 , 25 ). Since usually hospital services have lower cost-sharing because of less price elasticity, by shifting services from outpatient to inpatient, a reduction in the quality of services due to inpatient complications is excepted. It will also increase the total cost of health. These results are consistent with the results of a study by Yoo et al, where the increase in cost-sharing for outpatient services led to an increase in hospitalization and health costs ( 24 ).

Fels in a model-based analysis showed that cost-sharing is a nonoptimal method because of the reduction of both essential and nonessential services (because of patients’ mistakes in distinguishing between essential and nonessentialservices) and reduction in access to health services ( 21 ). The results of this analysis are in line with the findings of the following studiesabout a reduction in the use of preventive care ( 45 , 47 , 54 ) reduction in medication adherence ( 45 ), and more sensitivity of low-income patients to cost-sharing for health care utilization ( 25 , 26 , 28 ). In this regard, value-based cost-sharing methods seek to eliminate the shortcomings, which also achieved positive results in this regard ( 56 ).

From the financial dimension, the effect of cost-sharing included a small to a significant reduction in health care costs( 12 , 32 , 36 ). Although the reduction of health costs is one of the most important positive findings of cost-sharing, the exposure to the following side effects in studies criticizes this achievement: shifting the financial burden to consumers ( 25 ), increasing the financial burden for consumers ( 16 ), and increasing health costs because of increased hospitalization ( 24 ). However, the results of empirical illustration showed that shift deductible plans reduce out-of-pocket payment costs( 60 ). Moreover, in response to these shortcomings, value-based cost-sharing schemes were proposed as optimal methods ( 64 - 66 ).As a final point, increasing the profits of insurance organizations because of the reduction of insured claims is another positive and significant consequence of cost-sharing ( 16 , 61 ).

Savings accounts are one of the means of financing and controlling consumer moral hazard and are also useful for future saving, which is implemented either compulsorily or voluntarily ( 18 ). Despite this function, the results of a study showed negative results in reducing health costs and reducing moral hazard in China, which the authors consider a result of the compulsory membership and social participation in the project,being less valuable compared with out-of-pocket payments.( 67 ). Furthermore, the results of a study on the effect of health savings accounts on savings and the promotion of preventive behavior showed that the members of this plan do not perform both savings and preventive behavior at the same time ( 68 ). In addition, the results of another study showed that savings are reduced in voluntary schemes with generous employers ( 54 ). Generally, the results of studies on the consequences of savings accounts on the consumption of health services and costs were different, which were expected to be like this because of mandatory and voluntary membership and type of administration in different countries.

The waiting list is an alternative to a user fee to reduce costs in countries with national health systems that control unnecessary demand by imposing the cost of time ( 14 ). The results of the included studies on the waiting list indicate that this intervention is not desirable from the perspective of patients( 70 ) and is nonoptimal ( 19 , 70 , 72 ). The waiting list reduces health costs by potentially encouraging high-income patients or patients with high waiting costs to select a private setting ( 72 ). Although reducing the costs through the choices of private sectors by high-income people is considered an advantage, the result of the analysis by Olivella showed that the presence of the private sector reduces the willingness of public sector providers to reduce waiting time ( 71 ).

Nonuse incentive schemes or bonus insurance often offers rewards in the form of a reduction in the next year’s premiums( 13 ) or generous coverage for the next contract ( 20 ). These interventions aim to promote healthy behavior, prevent high-risk behaviors, and control demand from the source ( 74 ) without access restricting ( 21 ). The findings of the included studies showed positive findings in the direction of the goals of these programs. However, the public acceptance of these methods in a qualitative study showed less justifiability of these methods compared to cost-sharing methods ( 74 ).

Limitations of the Study

This study had some limitations. The first limitation was the methodological diversity of the studies and their heterogeneity therefore the narrative synthesis approach was used to summarize the results of the studies. The second limitation was the possibility of language bias due to the limitation of non-English articles on publishing or indexing the results and the focus of this study on Persian and English articles which led to the absence of studies in other languages in the analysis of results. Another limitation was that the majority of the studies concentrated on the impacts of cost-sharing, with fewer studies looking at the effects of other demand-side interventions. Finally, there was the possibility of researcher bias in favor of a specific intervention, which might have influenced the study’s outcomes.

Demand-side interventions were designed to reduce consumer motivation for unnecessary consumption. The results of this study showed that each of these interventions has advantages and disadvantages. The most important strengths of these interventions, in general, include reducing the consumption of health services, especially outpatient services, and reducing health costs and third-party payers’ costs. The downsides of these approaches include a reduction in service quality, a transfer in a financial burden to consumers, and limited access, particularly for low-income populations. When looking at the outcomes of interventions, it becomes clear that the majority of the benefits, particularly in cost-sharing and waiting list interventions, benefit insurance companies and third-party payers, while the drawbacks of these interventions disproportionately burden consumers. Therefore, in regulating these interventions in health systems and insurance organizations, it is necessary to pay more attention to these consequences and their management, as a lack of attention in this regard may impair the performance of insurance financial protection and health provision as one of the major goals of health systems.

Acknowledgment

This study was part of a PhD thesis in health services management supported by Iran University of Medical Sciences (grant No: IUMS/SHMIS-1399-3-37-19512.). The authors would like to thank all the staff involved in the School of Health Management and Information Sciences and the research department of Iran University of medical sciences.

Ethical Approval

The study was approved by the local ethical committee of Iran University of Medical Sciences (code: IR.IUMS.REC.1399.1103).

Conflict of Interests

The authors declare that they have no competing interests.

Cite this article as: Koohi Rostamkalaee Z, Jafari M, Abolghasem Gorji H. Demand-side Interventions to Control Moral Hazard in Health Systems, Beneficial or Detrimental: A Systematic Review Study. Med J Islam Repub Iran . 2022 (27 Jun);36:69. https://doi.org/10.47176/mjiri.36.69

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• Health Insurance

The Affordable Care Act Affects Moral Hazard in the Health Insurance Industry

The Patient Protection and Affordable Care Act was signed into law by President Barack Obama in 2010. Commonly known as the Affordable Care Act (ACA) or Obamacare, the law expanded Medicaid, created health insurance exchanges, and included health-related provisions so millions of uninsured Americans could get health insurance .

Coverage was designed to be affordable under the ACA. The Act provided those with lower incomes with premium tax credits and cost-sharing reductions. The ACA inflated existing moral hazards in the health insurance industry by mandating coverage and community ratings, restricting prices, establishing minimum standards requirements, and creating a limited incentive to compel purchases.

Key Takeaways

• A moral hazard exists where one party in a contract assumes the risks associated with the other party without suffering any consequences.
• Moral hazards can be found in employee/employer relationships, in contracts between lenders and borrowers, and in the insurance industry between insurers and their clients.
• Before Obamacare, moral hazard was encouraged in health insurance with tax incentives that encouraged employer-based health coverage, placing consumers further away from medical costs.
• The ACA tried to cut back on the moral hazard of healthy people skipping health care coverage by imposing an individual mandate.

What Is a Moral Hazard?

The term moral hazard refers to a situation where one party has an incentive to use more resources than they otherwise would have because another party is bearing the costs. One party to a contract ultimately assumes risk to the other party without any consequences. The aggregate effect of moral hazard in any market is to restrict supply, raise prices, and encourage overconsumption.

Moral hazard has played a significant role in the health insurance segment of the economy . Moral hazards existed in the U.S. insurance markets before Obamacare but the Act's flaws exacerbate rather than alleviate those problems. There are no normative, morality-based elements to the economic sense of moral hazard.

Moral Hazard and Health Insurance

Many argue that health insurance itself is a moral hazard because it reduces the risks of pursuing an unhealthy lifestyle or other risky behavior. But this is only true if the costs to the customer, including insurance premiums and deductibles, are the same for everyone.

Insurance companies charge higher rates to riskier customers in a competitive market.

Moral hazard is largely removed when prices are allowed to reflect real information. Decisions to smoke cigarettes or go skydiving look different when they cause premiums to increase from $50 to $500 per month.

Insurance underwriting is crucial for this reason but many regulations that are designed to promote fairness end up clouding this process. Insurance companies raise all rates to compensate.

Moral hazard in health insurance in the United States was encouraged before Obamacare. Tax incentives have encouraged employer-based health coverage, placing consumers further away from medical costs. As economist Milton Friedman once stated, "Third-party payment has required the bureaucratization of medical care...the patient...has little incentive to be concerned about the cost since it's somebody else's money."

Moral Hazard and the Affordable Care Act

The ACA is 2,500 pages long so it can be very difficult to grasp its full impact. But here's a look at some of the basic provisions outlined in the law:

• Insurers can no longer deny coverage to those with pre-existing conditions.
• New government health insurance exchanges determine the type and cost of plans available to consumers.
• Large employers are required to offer employee health coverage.
• All plans must cover the 10 essential benefits of health insurance.
• Annual and lifetime limits on employer plans are banned.
• Plans are only considered affordable if the cost is less than 9.5% of family income.

The act also carried with it an individual mandate, a requirement that all uninsured Americans had to purchase a health insurance policy or pay a fine. But there were hardship exemptions in place to protect those who couldn't afford coverage.

This individual mandate was signed in 2010 and it went into effect in 2014. The idea behind it was that people who were fairly healthy would decline coverage so they could save the added expense of a health insurance premium. Insurance companies would raise rates to compensate for lost revenue , putting more financial stress on those who did have coverage. Anyone who didn't have coverage would pay the penalty by including it on their federal income tax returns.

2017 TCJA Changes

The individual mandate was ultimately repealed when the Tax Cuts and Jobs Act (TCJA) was passed in 2017. This law eliminated the fine imposed on people without health care coverage beginning in 2019. But there are still several states that require residents to have coverage despite the TCJA's terms.

The individual mandate was repealed after the Tax Cuts and Jobs Act was signed into law but several states require residents to carry health insurance coverage or face a fine.

Restricting costs, mandating employer coverage, and requiring minimum benefits further drive a wedge between the consumer and the real cost of health care. Premiums have predictably spiked since the passage of the Act, consistent with economic theory about moral hazard.

Is the ACA Still in Effect?

The Affordable Care Act is still in effect although the Tax Cuts and Jobs Act altered some of its provisions. More than 40 million Americans had obtained health insurance coverage under the ACA by early 2023. The figure set a record.

Are the ACA Premium Tax Credits Still Available Since the Passage of the TCJA?

Yes, but they're not and never have been available to everyone. Only American households with incomes between 100% and 400% of the federal poverty level can qualify for the credits.

What Other Industries Are Vulnerable to Moral Hazards?

Moral hazards can be found in employee-employer relationships, in the financial industry with contacts between lenders and borrowers, as well as in the insurance industry between insurers and their clients.

The Bottom Line

Moral hazard results when one party uses more resources than they otherwise would have because someone else is effectively paying for them. Moral hazard was effectively discouraged in health insurance when Obamacare was enacted in 2010 but the Tax Cuts and Jobs Act (TCJA) made changes to at least one key provision in 2017.

Hoover Institution. " How to Cure Health Care ."

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Page 83.

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Page 68.

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Page 54.

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Pages 45-46.

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Page 13.

Cornell Law School Legal Information Institute. " 26 U.S. Code § 36B. Refundable Credit for Coverage Under a Qualified Health Plan ."

U.S. Congress. " H.R.3590—Patient Protection and Affordable Care Act ." Pages 81-82.

HealthCare.gov. " Patient Protection and Affordable Care Act ."

Congressional Research Service. " The Individual Mandate for Health Insurance Coverage: In Brief ." Page 1.

Congressional Budget Office. " Repealing the Individual Health Insurance Mandate: An Updated Estimate ."

Healthcare.gov. " No Health Insurance? See if You'll Owe a Fee ."

National Conference of State Legislatures. " Health Insurance: Premiums and Increases ."

Office of the Assistant Secretary for Planning and Evaluation. " Health Coverage Under the Affordable Care Act: Current Enrollment Trends and State Estimates ."

U.S. Department of Health and Human Services. " About the Affordable Care Act ."

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Article Contents

1. introduction, 2. “moral hazard” in health insurance, 3. is there moral hazard in health insurance, 4. the nature of moral hazard in health insurance, 5. forecasting healthcare spending under counterfactual contracts, 6. conclusions, acknowledgements:.

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Moral Hazard in Health Insurance: What We Know and How We Know It

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Liran Einav, Amy Finkelstein, Moral Hazard in Health Insurance: What We Know and How We Know It, Journal of the European Economic Association , Volume 16, Issue 4, August 2018, Pages 957–982, https://doi.org/10.1093/jeea/jvy017

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We describe research on the impact of health insurance on healthcare spending (“moral hazard”), and use this context to illustrate the value of and important complementarities between different empirical approaches. One common approach is to emphasize a credible research design; we review results from two randomized experiments, as well as some quasi-experimental studies. This work has produced compelling evidence that moral hazard in health insurance exists—that is, individuals, on average, consume less healthcare when they are required to pay more for it out of pocket—as well as qualitative evidence about its nature. These studies alone, however, provide little guidance for forecasting healthcare spending under contracts not directly observed in the data. Therefore, a second and complementary approach is to develop an economic model that can be used out of sample. We note that modeling choices can be consequential: different economic models may fit the reduced form but deliver different counterfactual predictions. An additional role of the more descriptive analyses is therefore to provide guidance regarding model choice.

Empirical work in applied microeconomics is often loosely classified into two categories: “reduced form” or “structural”. 1 Although this classification is somewhat subjective, surely imperfect, and to some extent artificial—there is a richer spectrum of empirical approaches that could be broken down to many more than two categories—this simple classification is often used to imply two mutually exclusive approaches that are at odds with each other. And the researcher—faced with a question and a data set—is portrayed as needing to make an almost religious choice between the two approaches. In this paper we try to make the simple point—appreciated by many, but perhaps not all—that these two empirical approaches are in fact complements, not substitutes. Each has its own pros and cons. They should often be used in tandem (within or across papers) as scholars embark on answering a specific research question.

To illustrate this point, we use the specific topic of moral hazard in health insurance, on which there is a vast empirical literature (including our own) covering a range of empirical approaches. In the context of health insurance, the term “moral hazard” is widely used (and slightly abused) to capture the notion that insurance coverage, by lowering the marginal cost of care to the individual (often referred to as the out-of-pocket price of care), may increase healthcare use (Pauly 1968 ). In the United States—the context of all the work we cover in this paper—a typical health insurance contract is annual and concave. It is designed so that the out-of-pocket price declines during the year, as the cumulative use of healthcare increases.

We make no attempt to review the voluminous empirical literature on the topic. Rather, we select only a few specific papers—drawing (grossly) disproportionately on our own work—to illustrate the relationship and complementarities between different empirical approaches used to study the same topic. Our focus is thus not only on describing (some of) what we know, but also on how we know it.

We begin by defining the object of interest: what “moral hazard” means in the context of health insurance, and why it is of interest to economists. We then discuss work on three specific questions related to moral hazard in health insurance. First, we describe work that has tested whether moral hazard in health insurance in fact exists. There is a clear affirmative answer, with much of the most-convincing existing evidence coming from large-scale randomized experiments: Just like almost any other good, individuals increase their healthcare utilization when the price they have to pay for it is lower. Second, we describe work that tries to assess the nature of the consumer response. In particular, we ask whether individuals respond to the dynamic incentives that arise from the nonlinear health insurance contracts. Again, the general finding is positive, with much of the evidence driven by quasi-experimental studies. Finally, we describe work that attempts to forecast what healthcare spending would be under contracts we do not observe in the data. This requires a more complete model of individual behavior.

In the final section, we conclude by returning to our main goal in writing this paper, and discuss the cross-pollination across the methods and approaches used in the three preceding sections. Although all methods were used in the context of the same broad topic, the more specific questions they answer are slightly different. We highlight the value of each approach, and the important interactions between them. In particular, compelling “reduced form” causal estimates of the impact of health insurance contracts on healthcare spending are invaluable for testing specific hypotheses, such as whether there is any behavioral response or whether individuals respond to dynamic incentives. There are settings and questions in which such reduced form estimates may be sufficient. In particular, if the variation used is sufficiently close to prospective policies of interest, one might need to go no further. Yet, many—perhaps most—questions of interest require us to make predictions out of sample, for which economic models that rely on deeper economic primitives are important. These modeling choices should not be made in a vacuum; the descriptive evidence from the reduced form provides general motivation, as well as more specific guidance, as to which modeling choices are more appropriate in a given context.

We are clearly not the first to attempt to highlight the value of combining different empirical approaches in the context of the same question. Very similar views are expressed in Chetty ( 2009 ), Heckman ( 2010 ), Nevo and Whinston ( 2010 ), and Einav and Levin ( 2010 ), among others. Although tastes or skill sets of individual researchers may understandably lead them to disproportionately or exclusively pursue one particular style of empirical work, the literature as a whole benefits enormously from attempts to incorporate and cross-pollinate the two, within and across papers. Discussing these issues in the abstract is often difficult, so customizing the discussion to a specific context may be useful. Our modest goal in this paper is to provide such a specific context within which to illustrate this more general point.

Throughout this paper, we follow decades of health insurance literature and use the term “moral hazard” to refer to the responsiveness of healthcare spending to insurance coverage. The use of the term in this context dates back at least to Arrow ( 1963 ). Consistent with the notion of hidden action, which is typically associated with the term “moral hazard,” it has been conjectured that health insurance may induce individuals to exert less (unobserved) effort in maintaining their health. For example, Ehrlich and Becker ( 1972 ) modeled health insurance as reducing individuals’ (unobserved) effort in maintaining their health; because health insurance covers (some of) the financial costs that would be caused by poor health behaviors, individuals may have less incentive to avoid them—they may exercise less, eat more cheeseburgers, and smoke more—when they have insurance coverage.

However, this so-called “ex ante moral hazard” has received very little subsequent attention in empirical work from the literature. 2 This may be because it is not empirically relevant in many contexts—the increased financial cost associated with poor health is not the only cost, and probably not the most important cost of being sick.

The focus of the moral hazard literature has instead been on what is sometimes referred to as “ex post moral hazard”. That is, on the responsiveness of consumer demand for healthcare to the price she has to pay for it, conditional on her underlying health status (Pauly 1968 ; Cutler and Zeckhauser 2000 ). In that sense, the use of the term “moral hazard” is a bit of an abuse of the “hidden action” origin of the term. The “action”—that is, the individual’s healthcare utilization—is in fact observed (and contractible), and the asymmetric information problem may be more naturally described as a problem of “hidden information” (regarding the individual’s health status). Yet, to stay consistent with decades of abuse of terminology in the entire health insurance literature, we use the term in a similar way and by “moral hazard” refer to how consumer demand for healthcare responds to the out-of-pocket price the consumer has to pay for that care.

Consumer cost-sharing is the typical name used for determining the out-of-pocket price the consumer has to pay for healthcare. Because the set of healthcare services is broad, and the price of each service could vary, insurers often specify coverage as a percentage share of the total healthcare spending.   The share of total healthcare spending paid by the individual is referred to as “consumer cost-sharing”; the remaining share is paid by the insurer. For example, a 20% consumer co-insurance or cost-share means that for every dollar of healthcare spending, the consumer pays 20 cents out of pocket and the insurer pays 80 cents.

Typical health insurance contracts are annual and do not specify a constant consumer cost-share. Rather, they specify the consumer cost-sharing as a function of the cumulative (over the covered year) amount of healthcare spending. This function is typically concave. Figure  1 shows a stylized example of a typical contract. This example shows a concave, piece-wise linear schedule with three “arms”. In the first—the deductible range—the individual faces an out-of-pocket price of 100%; every dollar of healthcare spending is paid fully out of pocket. After the deductible is exhausted, which in this example occurs at $500 in total spending, the individual enters the “co-insurance” arm, where she faces a price of 10%; for every dollar of healthcare spending. Finally, once the individual has spent a total of $3,500 out of pocket (or $30,500 in total spending), she reaches the “out-of-pocket maximum” (also known as “stop loss” or “catastrophic coverage”) arm, at which point she faces no cost-sharing and has complete insurance coverage.

A typical health insurance contract in the United States. Figure shows a stylized annual health insurance contract, illustrating the mapping the contract creates from total medical spending to out of pocket medical spending. The x -axis shows total medical spending for the year and the y -axis shows the out-of-pocket medical spending for the year.

Moral hazard is of economic interest because it creates an obstacle to the consumption-smoothing purpose of insurance. Insurance is valuable because it creates a vehicle for transferring consumption from (contingent) states with low marginal utility of income (e.g., when one is healthy) to states with high marginal utility of income (e.g., when one is sick). The first best insurance contract would equalize marginal utility across different states; the existence of moral hazard makes it infeasible to obtain the first best. As Pauly ( 1968 ) first pointed out, if individuals’ healthcare utilization responds to the price they have to pay for it and the underlying health status is not contractible, the cost of providing insurance will rise and individuals may no longer be willing to pay the break-even price of full insurance. Therefore, as shown by Holmstrom ( 1979 ), the presence of moral hazard leads optimal insurance contracts to be incomplete, striking a balance between reducing risk and maintaining incentives.

A declining out-of-pocket price schedule (see, e.g., Figure  1 ) is a natural way to optimally trade off the goal of combating moral hazard through higher consumer cost-sharing with the goal of providing risk protection through lower consumer-cost sharing. Since the value of insurance is increasing in the total spending, it makes sense to provide a policy that provides greater protection when spending is greater. Although this concave feature is common in many health insurance contracts in the United States, we will also discuss in what follows settings where contracts deviate from this pattern.

The existence, magnitude, and nature of the moral hazard response is thus a key input into the optimal design of private or public health insurance contracts. This is a natural reason for the study of moral hazard to attract the considerable theoretical and empirical attention that it has. However, moral hazard in health insurance has also attracted academic and policy interest for the potential it raises that higher consumer cost-sharing could help reduce the high—and rising—levels of healthcare spending as a share of GDP in most developed countries. This has prompted, for example, policy interest in high-deductible health insurance plans in the United States as a way of reducing aggregate healthcare spending levels. The majority of healthcare spending, however, is accounted for by a small share of high-cost individuals whose spending is largely in the “catastrophic” range where deductibles and co-payments no longer bind. This suggests that—for meaningful impacts on health care spending—the incentives for health insurance for providers—rather than for consumers—may be more important; we discuss this briefly in the conclusion.

We now know what moral hazard in health insurance is (or at least what we have all come to call it) and why it could be important for affecting the optimal design of health insurance contracts. But does it exist? Does health insurance actually increase healthcare spending? Health insurance, by design, lowers the price individuals pay for their medical care. First-year economics teaches us that demand curves tend to slope down, that when we make something cheaper, people tend to buy more of it. So the answer may seem obvious. Yet, in the context of healthcare, there are (at least) two views that are less sure.

One view holds that healthcare cannot be analyzed like any other good. Demand for healthcare, in this view, is determined by “needs”, not by economic factors, or as an economist might put it, the demand for healthcare is completely inelastic with respect to its price. Gladwell has expressed this view forcefully in a New Yorker article tellingly entitled “The Moral Hazard Myth”. 3 Expounding his central premise—that the “myth” of moral hazard in health insurance is a singularly American obsession that has created our singular lack of universal coverage—he writes “The moral hazard argument makes sense … only if we consume healthcare in the same way that we consume other consumer goods, and to [some] … this assumption is plainly absurd. We go to the doctor grudgingly, only because we’re sick.”

There is also a second view, according to which the demand for healthcare in fact slopes up! One version of this conjecture is that health insurance will improve people’s health by increasing timely and effective medical care (e.g., preventive care or better management of chronic conditions), and that this improved health will in turn reduce healthcare utilization. Another version points to the efficiency of healthcare use as a channel through which healthcare spending will fall when insurance coverage becomes more generous. For example, although most healthcare providers in the United States can choose whether or not to see patients, emergency rooms cannot; the Emergency Medical Treatment and Active Labor Act (EMTALA) requires that hospitals provide emergency medical treatment to all patients. There is therefore widespread speculation that one of the benefits of providing health insurance to previously uninsured individuals is to get them out of the expensive emergency room and into cheaper primary care (State of Michigan 2013 ). 4 Indeed, this idea that insuring the uninsured will reduce expensive (and presumably inefficient or unnecessary) emergency room visits has been a leitmotif of advocates of expanding health insurance coverage in the United States. For example, in making the case that Michigan should expand Medicaid coverage under the Affordable Care Act, Republican Governor Rick Snyder’s policy team argued “Today, uninsured citizens often turn to emergency rooms for nonurgent care because they don’t have access to primary care doctors—leading to crowded emergency rooms, longer wait times and higher cost. By expanding Medicaid, those without insurance will have access to primary care, lowering costs and improving overall health” (State of Michigan 2013 ).

We thus have three widely circulated competing claims: health insurance increases, decreases, or does not change healthcare spending. Research allows us to move from rhetoric to reality. Ultimately, the existence and sign of any moral hazard effects of health insurance is an empirical question. It is a challenging empirical question because people who have more generous health insurance presumably differ in other ways from people with less generous health insurance, and these differences may be correlated with expected healthcare spending. Indeed, the basic theory of adverse selection suggests that those who have more health insurance are on average in worse health (and hence face higher expected healthcare spending) than those with less health insurance (Akerlof 1970 ; Rothschild and Stiglitz 1976 ; Einav and Finkelstein 2011 ). How to separate such potential selection effects from the treatment effect of interest, namely moral hazard?

We describe evidence from two randomized evaluations of the impact of health insurance on healthcare spending: the RAND Health Insurance Experiment from the 1970s, and the 2008 Oregon Health Insurance Experiment. We review the evidence from each, which shows that moral hazard exists: health insurance increases healthcare spending. We then describe quasi-experimental evidence of moral hazard that uses the existence of “bunching” at a convex kink in the budget set created by the health insurance contract to also establish the presence of moral hazard (i.e., a behavioral spending response to the contract). We discuss the institutional setting for the RAND Experiment and the “bunching” estimator in some detail, since we will describe further analyses of both these settings in more depth in subsequent sections.

3.1. Two Randomized Evaluations

The oregon health insurance experiment..

In 2008, the state of Oregon engaged in a limited expansion of one of its Medicaid programs. Medicaid is the public health insurance program for low-income individuals in the United States. The particular program in Oregon was available to low-income, uninsured adults, aged 19–64, who were not already eligible for Medicaid by virtue of meeting one of its categorical requirements. This Medicaid program provided comprehensive health insurance coverage with zero consumer cost-sharing. Faced with budgetary constraints that precluded their offering the program to all eligible individuals, policymakers in the state of Oregon decided that a random lottery drawing would be the fairest way to allocate their limited Medicaid slots. The lottery was publicly advertised, and eligible individuals were encouraged to sign up. About 75,000 individuals signed up for the lottery, from which approximately 30,000 were randomly selected. Those who were selected won the ability to apply for Medicaid, and to subsequently enroll in Medicaid if found eligible. About 60% of those selected sent in applications, and about half of those applications were deemed eligible for Medicaid, resulting in about 10,000 individuals who won the lottery and enrolled in Medicaid. The remaining 45,000 who were not selected by the lottery became the control group; they were essentially unable to apply for Medicaid. About two years after the 2008 lottery, the state found additional resources and began to offer the ability to apply to Medicaid to those in the control group.

The lottery created the opportunity to use a randomized controlled design to study the effects of Medicaid coverage over its first two years. Specifically, random assignment by the lottery can be used as an instrument for Medicaid coverage (Imbens and Angrist 1994 ). Over the approximately two-year study period, lottery assignment increased the probability of having health insurance coverage by about 25 percentage points. Using this experimentally induced variation in insurance coverage, researchers have studied the short-term effects of Medicaid on a wide range of outcomes. The evidence indicates that Medicaid increases healthcare spending, improves economic security, and improves some health measures. We focus here on a subset of the healthcare spending results. 5

The results from the experiment show that Medicaid increases healthcare spending across the board, including hospital admissions, emergency department visits, primary care, preventive care, and prescription drugs. Illustrating a subset of these findings, Figure  2 shows the increased use of the emergency department (top panel) and the increase in primary and preventive care (bottom panel). Both panels plot the mean of the control group against that mean plus the “local average treatment effect” estimate of Medicaid, that is, the estimate of the impact of Medicaid on the outcome, using winning the lottery as an instrument for Medicaid coverage. For example, the estimates indicate that Medicaid increases the probability of having a primary care visit in the last 6 months by 21 percentage points, or over 35% relative to the control group’s mean, and the probability of having a recommended mammogram in the last 12 months by 19 percentage points, or about 65%. A back-of-the-envelope calculation using the induced increases in healthcare utilization suggests that, in the first year, Medicaid increases annual healthcare spending by about $775, or about 25% per year (Finkelstein et al. 2012 ).

Selective results from The Oregon experiment. Figure shows selected results from the Oregon Health Insurance Experiment. “Control mean” shows mean for lottery participants who were not selected. “Treatment effect” represents the IV estimate of the impact of Medicaid, using selection by the lottery as an instrument for Medicaid coverage (the first stage is about 0.25). 95% confidence intervals are shown with the whisker plot. Top panel shows results for Emergency Room use based on administrative data in the 18 months following the lottery (Taubman et al. 2014 ). Bottom panel shows results for primary and preventive care based on a mail survey administered 43 approximately one year after the lottery (Finkelstein et al. 2012 ).

The effect appears to operate across all types of care, with estimated increase in both “high value” care (such as preventive care) as well as in potentially “low value” care (such as emergency room visits for nonemergency conditions). 6 Indeed, contrary to the argument that Medicaid would decrease emergency department visits, the evidence indicates that Medicaid in fact increased emergency department visits by 40%; this increase in emergency department visits occurs across all kinds of patients (e.g., those who had used the emergency room frequently prior to the experiment and those who had not recently been) and all kinds of visits (e.g., on-hours care and off-hours care, or care classified as “emergency” and care classified as “non emergency”), and is persistent across the two years of the study (Taubman et al. 2014 ; Finkelstein et al. 2016 ).

The finding that Medicaid increases use of the emergency department was greeted with considerable attention and surprise (e.g., Heintzman et al. 2014 ). 7 Conceptually, however, the result should not be surprising. EMTALA requires hospitals to provide emergency care on credit and prohibits them from delaying treatment to inquire about insurance status or means of payment. Hospitals, however, can—and do—charge the patient for such visits, and Medicaid coverage reduces the out-of-pocket price of the visit to zero, presumably leading to an increase in demand for emergency department visits. At the same time, Medicaid coverage also reduces the price of other care to zero, generating additional, indirect effects, which could go in either direction. Many conjecture that primary care can substitute for emergency department care, and thus cheaper primary care may lead to a reduction in emergency department visits. Yet, the effect could also go in the other direction; for example, one may be more likely to seek emergency room care if one has insurance to cover any recommended follow up treatments. Since the Oregon experiment did not independently vary the price of primary care and emergency department care, it is not designed to address whether the emergency department and primary care are substitutes or complements. But the results indicate that, on net, Medicaid increases emergency department use, suggesting that any substitution that may exist is not large enough to offset the direct effect of making the emergency department free.

The RAND Health Insurance Experiment.

The Oregon Health Insurance Experiment examined the impact of insurance compared to no insurance. A separate question is whether, among those with health insurance, the comprehensiveness of that insurance affects healthcare utilization. Over three decades earlier, in the late 1970s, the RAND Health Insurance Experiment experimentally varied the extent of consumer cost-sharing across about 2,000 nonelderly families in order to study the effect of consumer cost-sharing in health insurance on healthcare spending and health. As before, we focus on the results for healthcare spending only. 8

Unlike the Oregon experiment, which was conceived of by policymakers for fairness purposes and capitalized on by academics for research purposes, the RAND Health Insurance Experiment was prospectively designed by researchers to estimate the impact of consumer cost-sharing. Families were randomly assigned to plans for 3–5 years. The plans differed solely in their consumer cost-sharing; for example, one plan had zero cost-sharing (“free plan” ) whereas others had 25%, 50%, or 95% cost-sharing (two others set different cost sharing based on the type of care). Importantly, all plans had an out-of-pocket maximum in order to limit the financial exposure of participants; above this maximum amount, families in all plans had full insurance. Thus, referring back to Figure  1 , the RAND plans had two of the three coverage arms shown: the coinsurance arm (with coinsurance ranging from zero to 95%), and the catastrophic arm that provides full coverage. The out-of-pocket maximum amounts were set at a fairly low level, so that even the least generous plan had substantial coverage. The exact amount of the out-of-pocket maximum was itself randomly assigned within each co-insurance assignment. The top panel of Figure  3 shows some examples of plans from the RAND experiment. We will return to this aspect of the design in subsequent discussion.

Contracts and outcomes in the RAND experiment. Top panel shows several of the contracts that were randomly assigned to different families in the RAND health insurance experiment; these contracts vary both in their co-insurance and (within coinsurance rates) in their out-of-pocket maximum. Bottom panel reports the estimated treatment effects of the different plans (defned by their coinsurance rate) on the probability of the individual having any medical spending in the year. Source: Aron-Dine et al. 2013 , Table 2 (see notes therein for more details).

Once again, the results from the randomized evaluation clearly point to the existence of a moral hazard effect. Lower consumer cost-sharing leads to more spending. The bottom panel of Figure  3 provides a flavor of these results, showing how the share of individuals with any annual healthcare spending decreases as the health insurance coverage becomes less generous.

3.2. Quasi-Experimental Evidence: Bunching in Medicare Part D

In addition to the randomized evaluations, a very large number of quasi-experimental studies also show that health insurance coverage is associated with increased healthcare spending. Here we focus on one such example, which is based on prescription drug spending responses to the Medicare Part D prescription drug benefit. It will serve as a subsequent point of departure for the modeling of spending under alternative contracts that is the focus of Section 5.

Medicare Part D was launched in 2006 to add prescription drug coverage to the existing Medicare public health insurance program for the elderly and disabled in the United States. In 2015, Medicare Part D covered about 42 million individuals and generated approximately $77 billion in budgetary outlays (Congressional Budget Office 2015). The original Medicare program – introduced in 1965 to cover hospital and physician services—offers uniform, publicly provided coverage. Medicare Part D, by contrast, is provided by private insurers who are required to offer coverage that is actuarially equivalent or more generous than a government-designed standard benefit.

The top panel of Figure  4 shows the government-defined standard benefit design in 2008. It shows the highly nonlinear nature of the standard Part D contract. According to this contract, the individual initially pays for all expenses out of pocket, until she has spent $275 (in cumulative drug spending within the covered year), at which point she pays only 25% of subsequent drug spending until her total drug spending reaches $2,510. At this point the individual enters the famed “donut hole”, within which she must once again pay for all expenses out of pocket until total drug spending reaches $5,726, the amount at which catastrophic coverage sets in and the marginal out-of-pocket price of additional spending drops substantially, to about 7%.

Contract design and bunching in Medicare part D. This figure replicates Figure I and Figure II in Einav, Finkelstein, and Schrimpf ( 2015 ). Top panel shows the standard benefit design in 2008. “Pre-Kink coverage” refers to coverage prior to the Initial Coverage Limit (ICL) that is where there is a kink in the budget set and the gap, or donut hole, begins. As described in the text, the actual level at which the catastrophic coverage kicks in is defined in terms of out-of-pocket spending (of $4,050), which we convert to the total expenditure amount provided in the figure. Once catastrophic coverage kicks in, the actual standard coverage specifies a set of co-pays (dollar amounts) for particular types of drugs, whereas in the figure we use instead a 7% co-insurance rate, which is the empirical average of these co-pays in our data. Bottom panel displays the distribution of total annual prescription drug spending in 2008 for our baseline sample. Each bar represents the set of people that spent up to $100 above the value that is on the x -axis, so that the first bar represents individuals who spent less than $100 during the year, the second bar represents $100–$200 spending, and so on. For visual clarity, we omit from the graph the 3% of the sample whose spending exceeds $6,500. The kink location (in 2008) is at $2,510. N =1,251,984.

As noted, individuals may buy plans that are actuarially equivalent to, or have more coverage than the standard plan, so that the exact contract design varies across individuals. However, a common feature of these plans is the existence of substantial nonlinearities that are similar to the standard coverage we have just described. In particular, the location of the “donut hole” at the government-set kink location is typical of most plans, although some of these plans provide partial coverage within the donut hole region. Using data on Medicare Part D beneficiaries from 2007 to 2009, we estimated that a beneficiary entering the coverage gap experiences, on average, a price increase of almost 60 cents for every dollar of total spending (Einav, Finkelstein, and Schrimpf 2015 ).

As many economists have observed, the donut hole is incompatible with basic economic theory, which would imply greater coverage for greater financial loss, or a concave coverage function as in Figure  1 . The donut hole apparently arose as a political compromise between the objective of having a program in which even those who spend little on drugs receive benefits and the need to keep projected expenditures below the legislated cap (Duggan, Healy, and Scott Morton 2008 ).

Whatever its theoretical demerits or political origins, the donut hole has proved a boon for empirical research on the moral hazard effects of insurance. Standard economic theory suggests that, as long as preferences for healthcare and consumption are strictly convex and smoothly distributed in the population, we should expect the distribution of individuals’ spending to bunch at a convex kink point of their budget set. This suggests a natural test for a behavioral response to price. If moral hazard does not exist, individual spending will be distributed smoothly in the population. With moral hazard, bunching will be observed around the convex kink in the budget set at the start of the donut hole, where insurance becomes discontinuously less generous on the margin. 9 Indeed, the bottom panel of Figure  4 shows a histogram of total annual prescription drug spending in 2008. The response to the convex kink at the donut hole is apparent: there appears to be a noticeable spike in the distribution of annual spending around the kink location. Moreover, the government changes the kink location each year and the location of the bunching moves in virtual lock step as the location of the kink moves. Across all years, we estimate that the convex kink leads to a statistically significant 29% increase in the density of individuals whose annual spending is around the kink location (Einav et al. 2015 ).

4.1. What is “The Price” of Medical Care in The Presence of Nonlinear Contracts?

We view the results summarized in the last section as presenting compelling evidence that moral hazard in health insurance exists: healthcare spending is higher when insurance coverage increases. This evidence seems a natural and necessary pre-condition for spending time and effort to model what spending would be under alternative contracts. This is one—presumably simple and obvious but important nonetheless—way in which reduced form work can complement economic modeling.

Yet, the evidence we have shown thus far provides little guidance regarding the nature of this moral hazard response or, relatedly, regarding the appropriate economic model to apply to the data. The nonlinear nature of virtually all health insurance contracts in the United States raises a key modeling question: what is the price of healthcare perceived by the insured individual as she contemplates using a specific healthcare service? Put differently, to what extent do individuals respond to the dynamic incentives that are generated by the nonlinearity of the health insurance coverage?

Until recently, this question had attracted relatively little attention in the moral hazard literature. Instead, a large number of empirical studies endeavored to summarize the impact of health insurance on healthcare utilization by reporting the price elasticity of the demand for medical care with respect to “the” out-of-pocket price. A review article by Cutler and Zeckhauser ( 2000 ), for example, summarizes about 30 such studies. A particularly famous and widely used estimate is the RAND Health Insurance Experiment’s estimate of the price elasticity of demand for medical care of −0.2 (Manning et al. 1987 ; Keeler and Rolph 1988 ).

However, in the presence of nonlinear contracts, applying such single elasticity estimates is challenging without some guidance as to whether and how one can map a nonlinear insurance coverage into a single price. For example, one cannot extrapolate from estimates of the effect of co-insurance on healthcare spending to the effects of introducing a high-deductible health insurance plan without knowing how forward looking individuals are in their response to health insurance coverage and their beliefs about the distribution of future health shocks. A completely myopic individual would respond to the introduction of a deductible as if the price has sharply increased to 100%, whereas a fully forward looking individual with annual healthcare spending that are likely to exceed the new deductible would experience little change in the effective marginal price of care.

The original RAND investigators were, of course, acutely aware of this issue and spent considerable effort estimating and modeling how individuals respond to the nonlinear incentives in the RAND contracts (Keeler and Rolph 1988 ). However, application of their −0.2 estimate in a manner consistent with their model is a nontrivial manner. Although notable exceptions exist (e.g., Buchanan et al. 1991 ; Keeler et al. 1996 ), most subsequent researchers have applied the RAND estimates in a much simpler fashion: they summarized the nonlinear insurance contracts with a single price to which the −0.2 elasticity was applied. For example, researchers used the average out-of-pocket price (Newhouse 1992 ; Cutler 1995 ; Cogan, Hubbard, and Kessler 2005 ; Finkelstein 2007 ), the realized end-of-year price (Eichner 1998 ; Kowalski 2016 ), or the expected end-of-year price (Eichner 1997 , Chap. 1) as various ways to summarize the nonlinear contract with a single number.

These choices can be consequential for the magnitude of the predicted spending response. Consider for example an attempt to forecast the effect of changing the plan from the RAND plan with a 25% coinsurance plan (and its associated, randomly assigned out-of-pocket maximums) to a plan with a constant 28% coinsurance plan. The price of medical care under the constant 28% coinsurance plan is well-defined (0.28). But in order to directly apply the RAND estimate of −0.2, we would also need to summarize the nonlinear RAND plan with a 25% coinsurance and a given out of pocket maximum with a single price; this essentially means choosing the weights to construct an average price. In Aron-Dine et al. ( 2013 ) we showed that three different ways to map the nonlinear RAND contract to a single price lead to out-of-sample spending predictions for the 28% constant co-insurance contract that vary by a factor of 2.

This shows that more work and care is needed to thoughtfully apply out-of-sample the results from even a justifiably famous and well-designed randomized experiment. Although the RAND health insurance experiment was prospectively designed to analyze the impact of cost sharing, at the end what it delivers is estimates of the causal effect of specific (nonlinear) health insurance plans. In order to move beyond what the experiment directly delivers—estimates of specific plans’ “treatment effects”—more assumptions regarding an economic model of behavior are needed. The RAND estimates continue to be used to this day in forecasting the effects of actual and proposed policies. Given the hard work that went into deriving those credible reduced form estimates, it seems hard to argue with devoting a commensurate amount of effort to considering how one might sensibly transform them out of sample.

4.2. Do Individuals Respond to Dynamic Incentives?

Once we recognize that the treatment of the nonlinear budget set can be consequential for this out-of-sample translation, the first question is whether in fact individuals take the dynamic incentives that are associated with the nonlinear budget set into account. A fully rational, forward-looking individual who is not liquidity constrained should take into account only the future price of medical care and recognize that (conditional on that future price) the current spot price on care is not relevant, and should not affect healthcare utilization decisions. However, there are a number of reasons why individuals might respond only to the spot price. They may be (or behave as if they are) unaware of or not understand the nonlinear budget set created by their health insurance contract, they may be affected by an extreme form of present bias and behave as if they are completely myopic, or they may wish to factor in the future price but be affected entirely by the spot price due to liquidity constraints.

The ideal way to test the null hypothesis of whether dynamic incentives matter would be to hold the spot price of care constant while varying the future price of care. As it turns out, the RAND Health Insurance Experiment did exactly that! As mentioned in Section  3 (see Figure  3 ), the RAND experiment randomly assigned the co-insurance rate across families and then, within each coinsurance rate, randomly assigned families to different levels of the out-of-pocket maximum. In principle, this is precisely the variation needed to test the null of whether individuals respond to the dynamic incentives: one would want to compare the initial healthcare utilization decisions of individuals randomized into plans with the same coinsurance rate but different out-of-pocket maximum. In practice, however, this approach is hampered by the relatively small sample sizes in the RAND experiment as well as the relatively low levels of the plans’ maximum amounts (Aron-Dine et al. 2015 ).

In the absence of the ideal experimental variation, in Aron-Dine et al. ( 2015 ) we instead take advantage of a particular feature of many U.S. health insurance contracts that generates quasi-experimental variation that is conceptually similar to this ideal. Most health insurance contracts are annual and reset on January 1, regardless of when coverage began. When individuals join a plan in the middle of the year, the deductible and other cost sharing features remain at the annual level, but are applied for a shorter coverage period. As a result, people who join the same plan in different months of the year face different contract lengths and therefore potentially different future prices, even though they all begin with the same spot price. A test of whether individuals respond to dynamic incentives then becomes whether individuals who join the same plan in different months of the year—and therefore face the same initial spot price of care but different future prices—have different initial healthcare utilization. We applied this idea in two settings: employer-provided health insurance and Medicare Part D. In both settings we were able to reject the null that individuals respond only to the spot price of care: individuals who faced the same spot price but higher future prices used less healthcare initially.

Figure  5 summarizes the nature of our findings in the Medicare Part D context. Medicare Part D annual plan choices are typically made during the open enrollment period in November and December, and provide coverage from January to December of the following year. However, when individuals become newly eligible for Part D at age 65, they can enroll in a plan the month they turn 65; the plan’s cost-sharing features reset on January 1, regardless of when in the year the individual enrolled. Variation in birth month thus generates variation in contract duration, and hence potentially in expected end-of-year price among individuals in a given plan in their first year.

Initial healthcare utilization and future price. This figure replicates Figure 2 in Aron-Dine et al. ( 2015 ). It graphs the pattern of expected end-of-year price and of any initial drug claim by enrollment month for individuals in Medicare Part D during their first year of eligibility (once they turn 65). We graph results separately for individuals in deductible plans and no deductible plans. We calculate the expected end-of-year price separately for each individual based on his plan and birth month, using all other individuals who enrolled in the same plan that month. The fraction with initial claim is measured as the share of individuals (by plan type and enrollment month) who had at least one claim over the first three months. =137,536 ( N = 108,577 for no deductible plans, and N = 28,959 for deductible plans).

Figure  5 shows future prices and initial claims for 65 year olds who enrolled in Medicare Part D between February and October. It shows the pattern of future prices and initial claims by enrollment month, separately for beneficiaries in two groups of plans: deductible and no-deductible plans (recall that the standard benefit design has a deductible, but insurers can offer more generous coverage than the standard design; many offer no-deductible options). We measure initial drug use by whether the individual had a prescription drug claim in the first three months of coverage. We summarize the dynamic incentives in the contract with the expected end-of-year price. The expected end-of-year price depends on three elements: the cost-sharing features of the beneficiary’s plan, the duration (number of months) of the contract (which in turn is determined by their birth month), and the beneficiary’s expected spending (which we calculate based on the spending of all individuals who enrolled in that plan in that month). Of course, if individuals do not believe their spending risk is drawn from the same distribution as everyone else who joined their plan in their month, there will be measurement error in the expected end-of-year price; similarly, if individuals are not risk neutral, other moments of the distribution of the end-of-year price may affect their initial utilization. Such modeling choices could be consequential if our goal were to estimate the extent of forward looking behavior. They may also bias us against rejecting the null of no forward looking behavior. However, if we do reject that null despite such potential sources of measurement error, it is informative.

The results provide evidence against the null that individuals do not respond to the future price. In the deductible plan, Figure  5 shows that the expected end-of-year price is increasing in the enrollment month; a later enrollment date gives the individual less time to spend past the deductible and into the lower consumer cost-sharing arm. Recall that all individuals in these plans face the same initial spot price of care; what varies is the contract length and thus the expected end-of-year price. In these plans, we see that initial utilization is decreasing with enrollment month. By contrast, in the no-deductible plan, the expected end-of-year price is decreasing with the enrollment month; here, a later enrollment date gives the individual less time to spend past the cost-sharing arm and into the donut hole. In these plans, by contrast, the probability of an initial claim does not appear to vary systematically with the enrollment month. Combined, the contrast suggests that, holding the spot price of care constant, initial healthcare use is decreasing in the expected end-of-year price. In other words, individuals appear to respond to the dynamic incentives.

The descriptive results from the last two sections suggest that individuals’ decision making regarding healthcare utilization responds to the insurance coverage, and that this response is affected by the dynamic incentives associated with the nonlinear health insurance contracts commonly offered in the United States. One clear implication of these results is that assuming that the spot price associated with a given medical treatment is the only relevant price is problematic. However, we cannot conclude from this evidence that consumers do not respond at all to the spot price. Indeed, there is evidence to the contrary: Brot-Goldberg et al. ( 2017 ) study the introduction of a high-deductible plan (where previously there was no deductible) and present evidence that suggests a response to the spot price as well: predictably sick consumers reduce their spending in response to the deductible, despite the fact that they are likely to end the year outside of the deductible range. They conclude that changes in the spot price—rather than the future price—are the primary drivers of the reduced spending they observe when the high deductible is introduced.

When individuals respond to both spot and future prices, summarizing a given contract with a single price is not a sensible option. Therefore, when researchers want to use the experimental (or quasi-experimental) results to provide predictions for spending under other, counterfactual contracts not seen in the data, a more complete behavioral model is needed. We undertook such exercises in two related papers (Einav, Finkelstein, and Schrimpf 2015 , 2017a ). Our goal was to analyze spending under alternative nonlinear Part D contracts, and our motivating point of departure was the bunching at the convex kink created by the donut hole, which we described earlier. We showed that two different—and in our subjective opinion “reasonable”—models could both match the observed bunching, but produce fairly different out-of-sample predictions. This underscores the importance of modeling choices in extrapolating out of sample. Ideally, other evidence can be brought to bear to guide model selection.

In our context, we developed two alternative, non-nested models. One natural approach we implement is to adapt the Saez ( 2010 ) framework to our context. In this influential paper, Saez ( 2010 ) showed how a stylized, static, frictionless model of labor supply can allow for a simple mapping from the observed bunching around convex kinks in the income tax schedule to an estimate of the elasticity of labor supply. In Einav et al. ( 2017a ) we translated Saez’s model of labor supply to a model of prescription drug spending and applied his approach straightforwardly to the Medicare Part D setting. To do so, we assumed that individual i has quasi-linear utility in drug spending ( m ) and residual income ( y ): u i ( m, y ) =  g i ( m ) +  y . We chose a particular functional form for g i ( m ) so as to obtain a constant elasticity form for drug spending as a function of the out-of-pocket price that would be similar to Saez’s constant elasticity form for hours of work with respect to the after-tax wage. This allowed us to almost exactly follow his strategy and derive a mapping between the observed extent of bunching around the donut hole and the elasticity of drug spending with respect to the out-of-pocket price. This exercise resulted in an estimated elasticity of drug spending with respect to the out-of-pocket price of about −0.05. Because this is based on the bunching at the kink in annual drug spending, the spot and the future price of care are the same for the “bunchers” at the end of the year, which makes this a well-defined object.

Of course, the simplicity of the Saez-style approach comes at the cost of potentially abstracting from a host of real-world features that may be important in a particular context. Our real-world problem is dynamic: individuals make sequential purchase decisions throughout the year as information is revealed, and they make current healthcare utilization decisions facing uncertain future health shocks. The reduced form evidence we discussed in the previous section suggests that individuals do not ignore the future in making such decisions. This reduced form evidence has implications for model selection. In particular, it suggests that a static model—such as our adaptation of Saez ( 2010 )—may miss some important features of the consumer problem.

We therefore also developed a dynamic model of drug use in which a (potentially) forward looking individual facing uncertain future health shocks makes drug purchase decisions (Einav et al. 2015 ). We modeled weekly drug spending decisions, where each week there is some chance of a health event that could be treated by a prescription; if it occurs, the individual must decide whether or not to fill the prescription that week. The individual is covered by a nonlinear prescription drug insurance contract over 52 weeks. A coverage contract is given by a function, similar to the one depicted in the top panel of Figure  4 , that specifies the out-of-pocket amount the individual would be charged for a prescription drug with a given list price given the cumulative out-of-pocket spending up until that point in the coverage period. Optimal behavior can be characterized by a simple finite horizon dynamic problem. The three state variables are the number of weeks left until the end of the coverage period, the total amount spent so far, and a health state, which accounts for potential serial correlation in health.

In this model there are three economic objects. The first is a statistical description of the distribution of health shocks. The second key object is the primitive price elasticity, or “moral hazard”, that captures contemporaneous substitution between health and income. The third object captures the extent to which individuals understand and respond to the dynamic incentives associated with the nonlinear contract. As discussed in the last section, there is evidence that this response exists. The model allows us to quantify it, and to translate it into implications for annual drug spending under alternative—potentially counterfactual—contracts.

We parameterized the model with distributional and functional form assumptions and estimated it using simulated minimum distance. Importantly, one of the moments we fit is the extent of bunching around the donut hole. We then used the estimates to simulate the spending response to a uniform percentage price reduction in all arms of the standard, government-defined plan; this yields implied elasticities of about -0.25. This elasticity estimate is five times higher than what the Saez-style static model produced.

Thus, both the static Saez-style model and the dynamic model match, by design, the same observed bunching pattern, but they deliver very different out-of-sample predictions. The appeal of the Saez-style model is the simple and transparent mapping from the descriptive fact to the economic object of interest; relatedly, it can be implemented relatively quickly and easily. The dynamic model is more computationally challenging and time consuming to implement; it also has (despite our best efforts) more of a “black box” relationship between the underlying data objects and the economic objects of interest. However, it can account for potentially important economic forces that the static model abstracts from. In particular, it can account for anticipatory responses by forward looking agents to changes in the future price. The static model imposes that any response to the donut hole is limited to people around the donut hole. In contrast, the dynamic model allows for the possibility that the set of people near the donut hole—and therefore “at risk” of bunching—may in fact be endogenously affected by the presence of the donut hole; forward-looking individuals, anticipating the increase in price if they experience a series of negative health shocks, are likely to make purchase decisions that decrease their chance of ending up near the donut hole, even if at that point they are far from reaching it. Indeed, when we considered the implications in the dynamic model of “filling the donut hole” (i.e., providing 25% coinsurance in the donut hole instead of 100% coinsurance as scheduled under the Affordable Care Act to occur by 2020), we estimated that about one-quarter of the resultant spending increase came from “anticipatory” responses by individuals whose annual spending prior to this policy change would have been well below the donut hole (Einav et al. 2015 ).

The comparison of the results from the static and dynamic model highlights a broader point that should be neither novel nor surprising: modeling choices are consequential. In this specific application, we show that an in-sample bunching pattern may be rationalized by different modeling assumptions, and these assumptions can, at least in some contexts, have very different quantitative implications out-of-sample. This issue is not unique to the bunching literature. The phenomenon is more general. For example, our previous discussion of the results of the RAND Health Insurance Experiment illustrated that the assumptions made in translating the experimental treatment effects into economic objects that could be applied out of sample were also consequential.

More generally, the bunching literature following Saez ( 2010 ) is one specific application of the influential “sufficient statistics” literature popularized by Chetty ( 2009 )—which attempts to use simple models to directly and transparently map reduced form parameters into economic primitives. Our analysis illustrates that two different models can map the same reduced form object into very different out-of-sample predictions. Sufficient statistics, in other words, are sufficient conditional on the model (or set of models). This is an obvious point, made clearly by Chetty ( 2009 ), but sometimes forgotten in applications and interpretations.

The title (and purpose) of our paper is to discuss both “what we know” and “how we know it”. The research on moral hazard effects of health insurance that we described (hopefully) illustrates the claim we made at the outset: “reduced form” and “structural” work have their different strengths and limitations, and are most powerful when used in tandem (within or across papers) to answer a given question or a related set of questions.

The reduced form evidence tells us unambiguously that health insurance increases health care utilization and spending. Moral hazard, in other words, irrefutably exists. The overwhelming, compelling evidence on this point—from several randomized evaluations as well as countless, well-crafted quasi-experimental studies—should give any informed reader considerable pause when they hear claims to the contrary. Consider the rhetorical debate we started with over whether moral hazard exists and if so whether it might be of the opposite sign. These qualitative hypotheses are powerfully rejected by the reduced form evidence. This is a particular illustration of a broader point: when the debate is about sharp nulls, or qualitative signs, credible reduced form studies, which often rely on fewer modeling assumptions, are very powerful in convincingly distinguishing between competing hypotheses.

Reduced form evidence can also be valuable for retrospective analysis when an existing policy of interest is captured by the reduced form variation. If one is interested in the question: what happened when Oregon expanded Medicaid coverage in 2008, there is no better way to answer that than with the results of the lotteried expansion. Likewise, historical interest in the impact of the original introduction of Medicare can be well-served by reduced form analyses of the impact of that introduction (Finkelstein 2007 ; Finkelstein and McKnight 2008 ).

One might also be tempted to use reduced form results for prospective analyses of policies that are “close enough” to the reduced form variation. Here, however, it becomes challenging without additional theory and evidence to know what dimensions of the setting are important and how to judge “closeness” in those dimensions. For example, the low-income, able boded uninsured adults covered by Medicaid through the 2008 Oregon Health Insurance Experiment are a very similar population to the low-income able boded uninsured adults covered by the 2014 Medicaid expansions under the Affordable Care Act; indeed, the only obvious difference is that in Oregon eligibility required the individual to be below 100% of the federal poverty line whereas the state Medicaid expansions reached to 138% of the federal poverty line. Yet a host of factors could produce differential short-run impacts of Medicaid in Oregon and in these other expansions. The most obvious is that the demographics of low income adults and the nature of the healthcare system (including the healthcare safety net) differs across the country. One could perhaps shed some light on this (power permitting) through heterogeneity analysis in the Oregon experiment across types of people and places. Other observable differences—such as in the macro economy—would be harder to address. More subtle conceptual differences would require more thought and modeling. For example, the partial equilibrium impacts of covering a small number of people in Oregon might differ from the general equilibrium effects of a market-wide expansion in insurance coverage under the ACA (Finkelstein 2007 ). The impact of health insurance for individuals who voluntarily sign up for the lottery may well be different than the impact when, as in the ACA, insurance coverage is mandatory (Finkelstein et al. 2012 ; Einav et al. 2013 ).

The limitations of prospective policy analysis with reduced form evidence points to the need for economic modeling. More broadly, whenever we want to study the impact of something not observed in the data, we need a model to extrapolate from reduced form estimates to the setting of interest. The results from the RAND Health Insurance Experiment that we described illustrated this point. The RAND experiment delivers causal estimates of the spending impact of the particular health insurance contracts included in the experiment. The literature has since extrapolated from these plan fixed effects to forecast the spending effects of alternative contracts not observed in the data, such as high-deductible plans. As we have seen, the modeling choices made in such extrapolations are quite consequential for the translation of the reduced form estimates into spending forecasts. Since ad hoc choices of how to extrapolate from reduced form estimates to contracts not observed in the data can yield very different results, this suggests the value of more formal modeling in which one specifies and estimates a model of primitives that govern how an individual’s medical care utilization responds to the entire nonlinear budget set contracted by the health insurance contract.

This is a nontrivial exercise. It requires, among other things, estimating the individual’s beliefs about the arrival rate of medical shocks over the year, her discount rate of future events, and her willingness to trade off health and medical utilization against other consumption. Naturally, as we illustrated, the modeling choices themselves will be consequential, even when they can match the reduced form facts. Here, the reduced form evidence that individuals are at least partly forward looking can motivate the use of a dynamic model.

We thus see great complementarity between the reduced form analysis and economic modeling in ways that our examples have hopefully illustrated. Economic models allow us to get more bang for our reduced form buck—analyzing, for example, not just whether the current Part D contracts affect drug spending but forecasting what that spending would be like under alternative policies. In turn, reduced form evidence allows us to focus our questions—it is useful to verify that moral hazard exists before trying to model it—and make more informed modeling choices.

Naturally this basic point applies more broadly than our narrow context of moral hazard effects of health insurance. One closely related, and understudied application is to the behavioral response of healthcare providers to the financial incentives embodied in healthcare contracts. As we noted earlier, healthcare spending is extremely right skewed—about 5% of the population accounts for about 50% of healthcare expenditures (Cohen and Yu 2012 ). Therefore most healthcare spending is accounted for by individuals who have spent past their deductible and co-insurance arms and face little, if any, cost-sharing requirements. For affecting the aggregate level of healthcare spending, therefore, focusing on provider rather than consumer financial incentives may be more fruitful.

The impact of provider incentives in health insurance has, to date, received comparatively less empirical attention than the impact of consumer incentives. There is hope, however, that this may be changing. For example, Clemens and Gottlieb ( 2014 ) provide quasi-experimental estimates of how quantity and nature of healthcare supplied by physicians responds to changes in their reimbursement rate for that care. Eliason et al. ( 2016 ) and Einav, Finkelstein, and Mahoney ( 2017b ) provide evidence that hospitals’ decisions of when to discharge patients tend to “bunch” on and shortly after the length of stay that provides the hospital with a large jump in payments; they then interpret this provider response through the lens of an economic model that allows for assessments of behavior under counterfactual payment schedules. The empirical approaches we discussed here in the context of consumer incentives—and the strong complementarity across them—have natural application to provider incentives.

It is a great time to be an empirical economist. We have a rich tradition of economic modeling and structural estimation to draw upon. And we are the beneficiaries of an improved (and improving!) reduced form toolkit for identifying causal effects (Angrist and Pischke 2010 ).   Both can be applied to the large, and rich administrative data sets that researchers are increasingly accessing. By combining these approaches—within and across papers—our production possibility frontier will expand even further.

This paper is based on the Alfred Marshall Lecture delivered by Finkelstein at the EEA-ESEM meetings in Lisbon on August 24, 2017. We gratefully acknowledge support from the NIA for the underlying work discussed (R01AG032449; P30AG012810, RC2AGO36631, and R01AG0345151). We thank Neale Mahoney and Imran Rasul for helpful comments. Einav and Finkelstein are Research Associates at NBER.

The editor in charge of this paper was Imran Rasul.

The precise definitions of these two terms is not always clear but it is safe to say that most current empirical micro researchers would agree with Justice Potter Stewart’s assessment of hard-core pornography: “I know it when I see it.” The reader can judge for herself in the specific applications we discuss in what follows.

Spenkuch ( 2012 ) provides one of the few pieces of evidence on “ex ante moral hazard”. He re-analyzes King et al.’s ( 2009 ) randomized evaluation of the impact of encouraging individuals in some geographic areas of Mexico but not in others to enroll in the then-newly introduced catastrophic health insurance program for workers outside the formal sector, Seuguro Popular. Spenkuch ( 2012 ) finds some evidence of declines in preventive care, such as flu shots and mammograms, associated with experimentally induced greater insurance coverage.

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Brot-Goldberg et al. ( 2017 ) report qualitatively similar patterns in their (nonrandomized) analysis of the effect of the introduction of a high deductible in the context of employer-provided health insurance: it appears to reduce both “high value” and “low value” care similarly.

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Ethical Lapses in the Medical Profession

More from our inbox:, don’t cave, columbia, a florida book oasis, balloon release ban.

To the Editor:

Re “ Moral Dilemmas in Medical Care ” (Opinion guest essay, May 8):

It is unsettling, and dismaying, to read Dr. Carl Elliott’s account of moral lapses continuing to exist, if not thrive, in medical education. As a neurology resident in the early 1970s, I was assigned a patient who was scheduled to have psychosurgery.

He was a prisoner who had murdered a nurse in a hospital basement, and the surgery to remove part of his brain was considered by the department to be a therapeutic and even forward-looking procedure. This was despite its being widely discredited, and involving a prisoner who could not provide truly informed consent.

A fellow resident and I knew that refusing would almost certainly result in suspension or dismissal from the residency, so we anonymously contacted our local newspapers, whose reporting resulted in an overflow protest meeting, cancellation of the psychosurgery and legislative action placing conditions on the acceptance of informed consent by prisoners.

It is lamentable that even though bioethics programs are widely incorporated into medical education, moral and ethical transgressions remain a stubborn problem as part of medical structures’ groupthink.

As Richard Feynman has emphasized , doubt, uncertainty and continued questioning are the hallmarks of scientific endeavor. They need to be an integral element of medical education to better prepare young doctors for the inevitable moral challenges that lie ahead.

Robert Hausner Mill Valley, Calif.

I would like to thank Carl Elliott for exposing the “Moral Dilemmas in Medical Care.” There is a medical school culture that favors doctors as privileged persons over patients.

I can remember multiple patient interactions in medical school in which I thanked a patient for allowing me to examine them and apologized for hurting them during my exam of their painful conditions.

I was then criticized by attending physicians for apologizing to the patients. I was told, on multiple occasions, that the patient should be thanking me for the privilege of assisting in my education.

Medical training, in a medical school culture that favors the privilege of the medical staff over the rights and feelings of patients, needs to be exposed and changed.

Doug Pasto-Crosby Nashville The writer is a retired emergency room physician.

As a psychiatrist and medical ethicist, I commend Dr. Carl Elliott for calling attention to several egregious violations of medical ethics, including failure to obtain the patient’s informed consent. Dr. Elliott could have included a discussion of physician-assisted suicide and the slippery slope of eligibility for this procedure, as my colleagues and I recently discussed in Psychiatric Times .

For example, as reported in The Journal of Eating Disorders , three patients with the eating disorder anorexia nervosa were prescribed lethal medication under Colorado’s End-of-Life Options Act. Because of the near-delusional cognitive distortions present in severe anorexia nervosa, it is extremely doubtful that afflicted patients can give truly informed consent to physician-assisted suicide. Worse still, under Colorado law, such patients are not required to avail themselves of accepted treatments for anorexia nervosa before prescription of the lethal drugs.

Tragically, what Dr. Elliott calls “the culture of medicine” has become increasingly desensitized to physician-assisted suicide, nowadays touted as just another form of medical care. In the anorexia cases cited, informed consent may have been one casualty of this cultural shift.

Ronald W. Pies Lexington, Mass. The writer is on the faculty of SUNY Upstate Medical University and Tufts University School of Medicine, but the views expressed are his own.

Carl Elliot’s article on medical ethics was excellent. But it is not just in the medical profession that there exists the “subtle danger” that assimilation into an organization will teach you to no longer recognize what is horrible.

Businesses too have a culture that can “transform your sensibility.” In many industries executives check their consciences at the office door each morning. For example, they promote cigarettes; they forget they too breathe the air as they lobby against clean-air policies; they forget they too have children or grandchildren as they fight climate-friendly policies or resist gun-control measures. The list could go on.

In every organization, we need individuals to say no to policies and actions that may benefit the organization but are harmful, even destructive, to broader society.

Colin Day Ann Arbor, Mich.

Re “ Columbia’s Protests Also Bring Pressure From a Private Donor ” (front page, May 11):

Universities are meant to be institutions of higher learning, research and service to the community. They are not items on an auction block to be sold to the highest bidder.

Universities that sell off their policy platform to spoiled one-issue donors who threaten to throw a tantrum no longer deserve our respect. Grant-making foundations should not be grandstanding online. Give money, or don’t, but don’t call a news conference about it.

If Columbia caves, why should prospective students trust it as a place where they can go to become freethinkers and explore their own political conscience as they begin to contemplate the wider world and issues of social justice?

This is a real test of Columbia and its leadership. I do not envy its president, Nemat Shafik, who has few good choices and no way to make everyone happy. What she should not sell is her integrity, or the university’s. She should stand up to these selfish donors. Learn to say, “Thanks, but no thanks.”

Carl Henn Marathon, Texas

Re “ Book Bans? So Open a Bookstore ” (Arts, May 13):

Deep respect for the American novelist Lauren Groff and her husband, Clay Kallman, for opening the Lynx, their new bookstore in Gainesville, Fla. The store focuses on offering titles among the more than 5,100 books that were banned in Florida schools from July 2021 through December 2023.

To all the book clubbers and haters of bans: Order straight from the Lynx.

Fight evil. Read books.

Ted Gallagher New York

Re “ Keep a Firm Grip on Those Mickey Mouse Balloons. It’s the Law ” (front page, May 9):

Balloons are some of the deadliest ocean trash for wildlife, as mentioned in your article about Florida’s expected balloon release ban.

Plastic balloon debris poses a significant threat to marine life, often mistaken for food or becoming entangled in marine habitats, leading to devastating consequences for our fragile ocean ecosystems.

As the founder of Clean Miami Beach, an environmental conservation organization, I’m concerned about the impact of plastic pollution on Florida’s wildlife and coastal areas. Florida’s stunning beaches and diverse marine life are not only treasures to us locals but also draw millions of tourists each year.

Because of the dangers, intentional balloon releases have been banned in many cities and counties across the state. A poll released by Oceana showed that 87 percent of Florida voters support local, state and national policies that reduce single-use plastic. Gov. Ron DeSantis must waste no time in signing this important piece of legislation into law.

Our elected officials should continue to work together to address environmental issues so Floridians and tourists can enjoy our beautiful state without its being marred by plastic pollution.

Sophie Ringel Miami Beach

Moral Hazard, Adverse Selection and Health Expenditures: A Semiparametric Analysis

Theoretical models predict asymmetric information in health insurance markets may generate inefficient outcomes due to adverse selection and moral hazard. However, previous empirical research has found it difficult to disentangle adverse selection from moral hazard in health care. We empirically study this question by using data from the Health and Retirement Study to estimate a structural model of the demand for health insurance and medical care. Using a two-step semi-parametric estimation strategy we find significant evidence of moral hazard, but not of adverse selection.

We have benefited from the comments of Amy Finkelstein and participants of the Conference on Structural Models in Labor, Aging and Health, and the Seventeenth Annual Health Economics Conference. We acknowledge excellent research assistance from Ivan Shaliastovich, and especially Alvin Murphy. Bajari and Hong would like to thank the National Science Foundation for generous research support.

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Published Versions

Patrick Bajari & Christina Dalton & Han Hong & Ahmed Khwaja, 2014. " Moral hazard, adverse selection, and health expenditures: A semiparametric analysis, " The RAND Journal of Economics, vol 45(4), pages 747-763.

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Perspective

When pto stands for 'pretend time off': doctors struggle to take real breaks.

Mara Gordon

A survey shows that doctors have trouble taking full vacations from their high-stress jobs. Even when they do, they often still do work on their time off. Wolfgang Kaehler/LightRocket via Getty Images hide caption

A survey shows that doctors have trouble taking full vacations from their high-stress jobs. Even when they do, they often still do work on their time off.

A few weeks ago, I took a vacation with my family. We went hiking in the national parks of southern Utah, and I was blissfully disconnected from work.

I'm a family physician, so taking a break from my job meant not seeing patients. It also meant not responding to patients' messages or checking my work email. For a full week, I was free.

Taking a real break — with no sneaky computer time to bang out a few prescription refill requests — left me feeling reenergized and ready to take care of my patients when I returned.

But apparently, being a doctor who doesn't work on vacation puts me squarely in the minority of U.S. physicians.

Research published in JAMA Network Open this year set out to quantify exactly how doctors use their vacation time — and what the implications might be for a health care workforce plagued by burnout, dissatisfaction and doctors who are thinking about leaving medicine.

"There is a strong business case for supporting taking real vacation," says Dr. Christine Sinsky , the lead author of the paper. "Burnout is incredibly expensive for organizations."

Shots - Health News

Health workers know what good care is. pandemic burnout is getting in the way.

Researchers surveyed 3,024 doctors, part of an American Medical Association cohort designed to represent the American physician workforce. They found that 59.6% of American physicians took 15 days of vacation or less per year. That's a little more than the average American: Most workers who have been at a job for a year or more get between 10 and 14 days of paid vacation time , according to the U.S. Bureau of Labor Statistics.

However, most doctors don't take real vacation. Over 70% of doctors surveyed said they worked on a typical vacation day.

"I have heard physicians refer to PTO as 'pretend time off,'" Sinsky says, referring to the acronym for "paid time off."

Sinsky and co-authors found that physicians who took more than three weeks of vacation a year had lower rates of burnout than those who took less, since vacation time is linked to well-being and job satisfaction .

And all those doctors toiling away on vacation, sitting poolside with their laptops? Sinsky argues it has serious consequences for health care.

Physician burnout is linked to high job turnover and excess health care costs , among other problems.

Still, it can be hard to change the culture of workaholism in medicine. Even the study authors confessed that they, too, worked on vacation.

"I remember when one of our first well-being papers was published," says Dr. Colin West , a co-author of the new study and a health care workforce researcher at the Mayo Clinic. "I responded to the revisions up at the family cabin in northern Minnesota on vacation."

Sinsky agreed. "I do not take all my vacation, which I recognize as a delicious irony of the whole thing," she says.

She's the American Medical Association's vice president of professional satisfaction. If she can't take a real vacation, is there any hope for the rest of us?

I interviewed a half dozen fellow physicians and chatted off the record with many friends and colleagues to get a sense of why it feels so hard to give ourselves a break. Here, I offer a few theories about why doctors are so terrible at taking time off.

We don't want to make more work for our colleagues

The authors of the study in JAMA Network Open didn't explore exactly what type of work doctors did on vacation, but the physicians I spoke to had some ideas.

"If I am not doing anything, I will triage my email a little bit," says Jocelyn Fitzgerald , a urogynecologist at the University of Pittsburgh who was not involved in the study. "I also find that certain high-priority virtual meetings sometimes find their way into my vacations."

Even if doctors aren't scheduled to see patients, there's almost always plenty of work to be done: dealing with emergencies, medication refills, paperwork. For many of us, the electronic medical record (EMR) is an unrelenting taskmaster , delivering a near-constant flow of bureaucratic to-dos.

When I go on vacation, my fellow primary care doctors handle that work for me, and I do the same for them.

But it can sometimes feel like a lot to ask, especially when colleagues are doing that work on top of their normal workload.

"You end up putting people in kind of a sticky situation, asking for favors, and they [feel they] need to pay it back," says Jay-Sheree Allen , a family physician and fellow in preventive medicine at the Mayo Clinic.

She says her practice has a "doctor of the day" who covers all urgent calls and messages, which helps reduce some of the guilt she feels about taking time off.

Still, non-urgent tasks are left for her to complete when she gets back. She says she usually logs in to the EMR when she's on vacation so the tasks don't pile up upon her return. If she doesn't, Allen estimates there will be about eight hours of paperwork awaiting her after a week or so of vacation.

"My strategy, I absolutely do not recommend," Allen says. But "I would prefer that than coming back to the total storm."

We have too little flexibility about when we take vacation

Lawren Wooten , a resident physician in pediatrics at the University of California San Francisco, says she takes 100% of her vacation time. But there are a lot of stipulations about exactly how she uses it.

She has to take it in two-week blocks — "that's a long time at once," she says — and it's hard to change the schedule once her chief residents assign her dates.

"Sometimes I wish I had vacation in the middle of two really emotionally challenging rotations like an ICU rotation and an oncology rotation," she says, referring to the intensive care unit. "We don't really get to control our schedules at this point in our careers."

Once Wooten finishes residency and becomes an attending physician, it's likely she'll have more autonomy over her vacation time — but not necessarily all that much more.

"We generally have to know when our vacations are far in advance because patients schedule with us far in advance," says Fitzgerald, the gynecologist.

Taking vacation means giving up potential pay

Many physicians are paid based on the number of patients they see or procedures they complete. If they take time off work, they make less money.

"Vacation is money off your table," says West, the physician well-being researcher. "People have a hard time stepping off of the treadmill."

A 2022 research brief from the American Medical Association estimated that over 55% of U.S. physicians were paid at least in part based on "productivity," as opposed to earning a flat amount regardless of patient volume. That means the more patients doctors cram into their schedules, the more money they make. Going on vacation could decrease their take-home pay.

But West says it's important to weigh the financial benefits of skipping vacation against the risk of burnout from working too much.

Physician burnout is linked not only to excess health care costs but also to higher rates of medical errors. In one large survey of American surgeons , for example, surgeons experiencing burnout were more likely to report being involved in a major medical error. (It's unclear to what extent the burnout caused the errors or the errors caused the burnout, however.)

Doctors think they're the only one who can do their jobs

When I go on vacation, my colleagues see my patients for me. I work in a small office, so I know the other doctors well and I trust that my patients are in good hands when I'm away.

Doctors have their own diagnosis: 'Moral distress' from an inhumane health system

But ceding that control to colleagues might be difficult for some doctors, especially when it comes to challenging patients or big research projects.

"I think we need to learn to be better at trusting our colleagues," says Adi Shah , an infectious disease doctor at the Mayo Clinic. "You don't have to micromanage every slide on the PowerPoint — it's OK."

West, the well-being researcher, says health care is moving toward a team-based model and away from a culture where an individual doctor is responsible for everything. Still, he adds, it can be hard for some doctors to accept help.

"You can be a neurosurgeon, you're supposed to go on vacation tomorrow and you operate on a patient. And there are complications or risk of complications, and you're the one who has the relationship with that family," West says. "It is really, really hard for us to say ... 'You're in great hands with the rest of my team.'"

What doctors need, says West, is "a little bit less of the God complex."

We don't have any interests other than medicine

Shah, the infectious disease doctor, frequently posts tongue-in-cheek memes on X (formerly known as Twitter) about the culture of medicine. Unplugging during vacation is one of his favorite topics, despite his struggles to follow his own advice.

His recommendation to doctors is to get a hobby, so we can find something better to do than work all the time.

"Stop taking yourself too seriously," he says. Shah argues that medical training is so busy that many physicians neglect to develop any interests other than medicine. When fully trained doctors are finally finished with their education, he says, they're at a loss for what to do with their newfound freedom.

Since completing his training a few years ago, Shah has committed himself to new hobbies, such as salsa dancing. He has plans to go to a kite festival next year.

Shah has also prioritized making the long trip from Minnesota to see his family in India at least twice a year — a journey that requires significant time off work. He has a trip there planned this month.

"This is the first time in 11 years I'm making it to India in summer so that I can have a mango in May," the peak season for the fruit, Shah says.

Wooten, the pediatrician, agrees. She works hard to develop a full life outside her career.

"Throughout our secondary and medical education, I believe we've really been indoctrinated into putting institutions above ourselves," Wooten adds. "It takes work to overcome that."

Mara Gordon is a family physician in Camden, N.J., and a contributor to NPR. She's on X as @MaraGordonMD .

• American Medical Association

Why Soda is Bad for You: the Health Risks

This essay about the detrimental effects of soda consumption on health. It highlights the risks associated with excessive sugar intake, including diabetes and obesity, as well as the negative impact on dental health due to acidic content. Even diet sodas pose risks due to artificial sweeteners and their potential effects on metabolism and gut health. Furthermore, soda consumption has been linked to cardiovascular issues and may even affect skeletal integrity over time. The essay emphasizes the importance of limiting soda intake and opting for healthier alternatives like water or tea for hydration.

How it works

Effervescent soda, with its saccharine effervescence and invigorating allure, has entrenched itself as a dietary mainstay across the globe. Despite its ubiquity, a mounting body of research and expert testimonies unveil substantial health hazards inherent in habitual soda consumption. From saccharine-laden colas to artificially sweetened diet variants, these libations exert deleterious effects on physical well-being that extend well beyond transient energy surges.

One of the most disconcerting facets of soda is its prodigious sugar quotient. A mere 12-ounce can of regular soda harbors approximately 10 teaspoons of sugar, predominantly in the guise of high fructose corn syrup.

Such egregious sugar ingestion engenders an abrupt surge in blood glucose levels, eliciting a commensurate insulin surge from the pancreas. Repeated glycemic spikes precipitate insulin resistance over time, a harbinger of type 2 diabetes. Cumulative evidence underscores that individuals imbibing sugary beverages daily are markedly predisposed to diabetes onset.

Moreover, apart from diabetes, heightened sugar consumption is inextricably linked to corpulence. Liquid sucrose from soda fails to confer satiety akin to solid sustenance, fomenting augmented caloric intake overall. This calorific surplus accrues as adipose tissue, particularly visceral fat. Soda aficionados are disproportionately susceptible to corpulence, a condition concomitant with myriad health maladies such as cardiovascular ailments and metabolic derangement.

In addition to the perils of sugar, the acidic milieu of soda precipitates profound repercussions on dental integrity. Phosphoric acid and citric acid, prevalent constituents in sodas, corrode dental enamel and furnish an environment conducive to caries formation. The lofty sugar content exacerbates this predicament by furnishing a microbial banquet that further imperils dental health. Habitual soda indulgence invariably fosters accelerated dental caries and attendant odontological tribulations.

Even ostensibly healthier diet sodas, bereft of sucrose, harbor their own panoply of risks. Synthetic sweeteners like aspartame and sucralose have been implicated in metabolic perturbations that paradoxically augment appetite and cravings. Emerging evidence posits that habitual diet soda imbibers exhibit exacerbated weight gain vis-a-vis non-consumers over time. Certain investigations have raised apprehensions regarding artificial sweeteners’ deleterious effects on gut microflora, potentially compromising digestive and immune function.

The impact of soda on cardiovascular health represents another pressing concern. Epidemiological inquiries have evinced a correlative relationship between habitual sugar-laden beverage ingestion and heightened risks of hypertension, dyslipidemia, and cardiovascular morbidity. These sequelae are exacerbated by soda’s high caloric load and attendant adiposity often accompanying its consumption.

In addition to the myriad health risks enumerated above, phosphoric acid, utilized in soda formulations to bolster flavor and extend shelf life, may exact a toll on skeletal integrity. Phosphoric acid interferes with calcium assimilation, potentially engendering osteopenia or osteoporosis over time, especially when soda supplants calcium-rich beverages like milk in one’s dietary regimen.

In summation, the evidence proffered unequivocally attests to the deleterious ramifications of soda consumption, both regular and diet. Excessive sugar, acidity, artificial sweeteners, and chemical adjuncts coalesce to furnish a concoction deleterious to metabolic, cardiovascular, and odontological health. While sporadic soda imbibition may not elicit cataclysmic repercussions, habitual indulgence warrants circumspection. Healthful alternatives such as water, unadulterated tea, or sparkling water furnish hydration and invigoration sans jeopardizing one’s well-being.

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PapersOwl.com. (2024). Why Soda Is Bad for You: The Health Risks . [Online]. Available at: https://papersowl.com/examples/why-soda-is-bad-for-you-the-health-risks/ [Accessed: 17-May-2024]

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