moral hazard essay

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

Liran einav.

1 Stanford University

Amy Finkelstein

2 Massachusetts Institute of Technology

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.

1. Introduction

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.

2. “Moral Hazard” in Health Insurance

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.

3. Is There Moral Hazard in Health Insurance?

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. The Nature of Moral Hazard in Health Insurance

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.

5. Forecasting Healthcare Spending under Counterfactual Contracts

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.

6. Conclusions

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.

Acknowledgements:

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.

1 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.

2 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.

3 Gladwell, Malcolm (2005). “The Moral-Hazard Myth.” New Yorker , August 29.

4 Dudiak, Zandy (2013). “Pittsburgh Area Legislators React to Governor’s Budget Proposals.” Forest Hill Patch , February 6.

5 J-PAL ( 2014 ) provides a brief overview of the experiment and some of its findings. More details on the experimental design, as well as specific results can be found in the original papers: Finkelstein et al. ( 2012 , 2016 ), Baicker et al. ( 2013 , 2014 ), and Taubman et al. ( 2014 ).

6 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.

7 Beck, Melinda (2014). “Medicaid Expansion Drives Up Visits to ER.” Wall Street Journal , January 3; Tavernise, Sabrina (2014). “Emergency Visits Seen Increasing with Health Law.” New York Times, January 2.

8 Our discussion draws heavily on the overview and retrospective provided by Aron-Dine, Einav, and Finkelstein ( 2013 ). For more detail on the experimental design and results, readers should consult Newhouse ( 1993 ) and the many original research papers discussed and cited therein.

9 This idea that individuals will bunch at convex kinks in their budget set has been present in the literature since the late 1970s. In the last decade, the increased availability of large and detailed administrative data has helped spur an explosion of empirical work on bunching, initially in the context of labor supply responses to the nonlinear income tax schedule (e.g., Saez 2010 ), but also in other contexts. Kleven ( 2016 ) provides an excellent review of this growing literature.

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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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By John Cassidy

By Amy Davidson Sorkin

By Isaac Chotiner

Contributions to Insurance Economics pp 61–96 Cite as

Moral Hazard and Insurance Contracts

• Ralph A. Winter 3  

402 Accesses

18 Citations

Part of the book series: Huebner International Series on Risk, Insurance and Economic Security ((HSRI,volume 13))

This essay synthesizes and extends the theory of optimal insurance under moral hazard, with a focus on the form of insurance contracts. The simplest model illustrates the most fundamental result: that the market responds to moral hazard with partial insurance coverage. But this model is not general enough to predict the contractual form of this response. The most general model, the Principal-Agent model, yields mostly negative results. In extending the theory, I adopt an intermediate approach, distinguishing between moral hazard on the probability of an accident and moral hazard on the size of the loss. This approach generates predictions as to when deductibles, coinsurance and coverage limits will be observed. The essay reviews as well moral hazard with a partially informed insurer and dynamic models of moral hazard. It concludes with a discussion of open questions in the theory of moral hazard and insurance.

• moral hazard
• principal-agent

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Winter, R. (1990). “On Moral Hazard and Insurance Contracts,” mimeo. University of Toronto.

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Winter, R.A. (1992). Moral Hazard and Insurance Contracts. In: Dionne, G. (eds) Contributions to Insurance Economics. Huebner International Series on Risk, Insurance and Economic Security, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1168-5_3

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Guest Essay

Moral Hazard Has No Place in Addiction Treatment

By Maia Szalavitz

Ms. Szalavitz is a contributing Opinion writer who covers addiction and public policy.

In 2016, Rachel Winograd began to see methadone patients who relapsed or left the treatment program where she worked start overdosing and dying at unprecedented rates. The culprit was illicitly manufactured fentanyl, which is generally 50 times as strong as heroin — with some variants an astonishing 5,000 times as potent. Fentanyl had begun to overtake heroin in Missouri.

“We were just seeing people drop like crazy,” said Dr. Winograd. But to her utter shock, staff members did not distribute naloxone, which is also known as Narcan, a nasal spray or injection that can reverse opioid overdose, to try to save their lives.

While fighting to change this policy, she discovered that many counselors, police officers, emergency medical technicians and even some doctors believed that handing out naloxone would do more harm than good. It would “enable” continued addiction and deter treatment, she was told. Or, others said, reducing fatalities would increase risk-taking among people who were already using drugs — and encourage children to try heroin.

Dr. Winograd, who is now the director of addiction science at the University of Missouri-St. Louis’s Missouri Institute of Mental Health, had encountered a concept known as moral hazard, the idea that reducing exposure to the negative consequences of a risk makes people more likely to take that risk.

While this phenomenon is a demonstrable concern for regulators of financial institutions — the 2008 crash is one infamous example — there’s little evidence it holds true in matters of health and safety. Here, moral hazard is far more of a political cudgel than a proven principle. As we face the worst overdose death crisis in American history, we can’t allow moral panic over moral hazard to drive out policies that have proved to save lives.

The University of Chicago economist Sam Peltzman introduced the idea of moral hazard to health policy in 1975. His data, he claimed, showed that seatbelt laws backfire because when drivers feel safer they take more risks, canceling out any benefit. Also known as risk compensation, the concept rapidly caught on as an argument against regulation.

But later research (as well as a continued significant decline in fatalities per mile as safety improvements continued) poured cold water on his conclusions. Researchers occasionally find a small moral hazard effect that is rarely enough to outweigh benefits. However, in most studies in areas as diverse as the influence of bicycle helmets on rider speed and the human papillomavirus vaccine on teen sexual behavior , moral hazard simply isn’t observed.

Despite the evidence, this idea continues to haunt addiction debates — specifically over harm reduction policies like drug decriminalization, programs that provide clean needles to prevent infectious disease and naloxone distribution to reverse overdose.

Some economists claim to have evidence that moral hazard eliminates most positive effects of harm reduction and increases overdose deaths. They use a method called causal inference, which, when its measures are set appropriately, can show cause and effect, unlike the observational research typical in public health.

For example, a 2018 study led by the economist Jennifer Doleac reported that naloxone distribution led to a 14 percent increase in overdose deaths in the Midwest, leading the Washington Post columnist Megan McArdle to endorse its claims of moral hazard . A 2022 study by the Vanderbilt University economist Analisa Packham used similar methods to claim that clean needle programs (which also distribute naloxone) caused a 25 percent increase in opioid-related fatalities.

These findings generated enormous controversy because they run contrary to the overwhelming majority of public health data — as well as to the recommendations of experts from the World Health Organization and the Centers for Disease Control and Prevention . More recent studies should bring humility to those who rely on data to make causal claims about behavior that they do not study directly — and to those who see moral hazard in harm reduction.

Researchers who dug into Ms. Doleac’s data found that it relied on erroneous assumptions about when naloxone availability increased in states that were studied. This is a crucial error, because if naloxone availability didn’t rise when the paper claimed that it had, it could not have caused subsequent overdose deaths. The study also had other flaws that rendered its conclusions unreliable. Ms. Packham’s research exhibited similar measurement problems and could not explain why small expansions of syringe exchange programs supposedly caused harm while massive expansions did not.

Moreover, a study of over 1,300 drug injectors published in 2023 found no changes in drug risk behavior after naloxone distribution and education started. A randomized trial of co-dispensing naloxone with opioid prescriptions in Colorado pharmacies also found no moral hazard effect.

Claims that harm reduction programs encourage teenage drug use by making it less dangerous do not hold up, either. As naloxone access has boomed, misuse of heroin and prescription opioids by high school seniors has plummeted . In 2007, 0.9 percent of 12th graders reported taking heroin and 9.2 percent reported misusing prescription opioids; those figures were 0.1 percent and 1 percent in 2023.

To further understand why moral hazard is especially unlikely to affect overdose, it’s critical to know how people with addiction actually behave.

For example, one methadone patient described his periods of active addiction to Dr. Winograd this way: “Look, all the money I have that day I’m going to spend on dope. All the dope I have I’m going to use.” Hansel Tookes, who founded Florida’s first legal syringe services program, shared the same sentiments. “My patients tell me they spend every dime that they have made that day. And then they wake up and they do it again,” Dr. Tookes said.

Stories like these typify the experience of addiction. And this means that even if naloxone did make addicted people more likely to take riskier amounts, it wouldn’t matter because it doesn’t provide the money needed to obtain them. (One would think economists would consider the role of economics.)

But there’s another compelling reason that naloxone doesn’t cause moral hazard, which is evident to anyone who understands the extremely unpleasant nature of having an overdose reversed. Naloxone immediately causes a distressing withdrawal syndrome — the experience people with addiction overwhelmingly seek to avoid.

Dr. Winograd has found that the more educated people are about addiction and the more closely they work with such patients, the less likely they are to endorse moral hazard arguments. The police are more likely to have these concerns than emergency medical technicians, who in turn are more likely to worry about moral hazard than addiction medicine doctors.

While it is possible that under some circumstances, reducing harm might increase risk-taking, this concern should not stand in the way of access to medications proven to save lives. It makes sense to apply the idea of moral hazard to banking — where the data shows that bailing out investors can make financiers more likely to gamble, especially with other people’s money. It does not follow that we should use the theoretical possibility that reducing harm might increase risk-taking in some settings to gamble with people’s lives.

Maia Szalavitz (@maiasz) is a contributing Opinion writer and the author, most recently, of “Undoing Drugs: How Harm Reduction Is Changing the Future of Drugs and Addiction.”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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Moral hazard means the tendency of parties to take risks believing that they don’t have to pay to pay for the results of their actions. The idea that a person is protected in a certain way from the risk will make them act differently as opposed to when they didn’t have that protection. Insurance companies, therefore, worry that by compensating people to protect them from losses, they may be actually encouraging high-risk behaviors. This results in the insurance company paying more in claims. Simply put, neither the consequences nor the cost will be felt by the policyholder. The scenario I was in is when as an insured individual, I had a much more information about my health than the insurance company. Therefore, after contracting for insurance, I could use that information superiority to change my behavior in a way that benefits me. Because the cost of my healthcare is being paid for by a third party, I have an incentive not to use health care less economically. As an insured person, I have less motivation to take care of myself and adopt a healthy lifestyle so as to avoid the need for health care. The primary dimension of moral hazard in this scenario results from the behavior and actions of the insured. It is like the insurance discourages the insured person from taking preventive measures. There are expenses involved when taking precautions to avoid unforeseen loss. However, individuals who are fully insured see no reason to bear these costs since their insurance will fully cover the cost. This encourages the insured to behave recklessly. Moral hazard is, therefore, the health care needed by a party that is insured because of failure to take preventive actions so as to avoid the care. Insurance may also encourage the insured to obtain unnecessary health care. The existence of insurance involves the concepts of shifting risks and spreading risks. People who are prone to risks are willing to pay insurance premiums that are greater than the expected value of a certain risk so as to transfer that risk to the insurance.

Mitigating Moral Hazard

Another way to be efficient is to create insurance contracts that require that the loss be shared between the insured and the insurer. Insurance companies can share the loss with policyholders through copayments and deductibles. Insurance companies can leverage the power they have to control some primary activities to induce safer behavior. Insurers can refuse to issue liability coverage to a person who does not take steps to ensure their safety and take care of their health. Another way is refusal to renew an existing policy to a person who is seen to be reckless. The insurance company can also coach for safer conduct. They should educate the insured on ways of reducing and avoiding risks. They should provide programs for identifying and controlling risks. They should inspect and audit their clients, analyze their history of loss, how losses occur, identify causes of accidents and train them on how to avoid increases of premium.

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Understanding Moral Hazard

• Bank Bailouts
• Compensation

The Bottom Line

• Behavioral Economics

What Are Examples of Moral Hazard in the Business World?

Moral hazard is a situation in which one party engages in risky behavior or fails to act in good faith because it knows the other party bears the economic consequences of their behavior. Any time two parties come into an agreement with one another, moral hazard can occur.

Key Takeaways

• Moral hazard is a situation in which one party engages in risky behavior or fails to act in good faith because it knows the other party bears the economic consequences of their behavior.
• In business, moral hazard can occur when a company is bailed out by the government; the company's leaders know that they and their company don't have to shoulder the responsibility of their risky decisions, the taxpayers do.
• Moral hazard means those who pay the costs have limited information about the other party they are transacting with.

Any time an individual does not have to suffer the full economic consequences of a risk, moral hazard can occur.

For instance, a driver in possession of a car insurance policy may exercise less care while operating their vehicle than an individual with no car insurance. The driver with a car insurance policy knows that the insurance company will pay the majority of the resulting economic costs if they have an accident.

In the business world, one way moral hazard can occur is when a company makes decisions knowing it won't have to bear the responsibility of the risk. When governments decide to bail out large corporations, for instance, the corporation doesn't have to bear the consequences of its decisions, the government will.

Moral Hazard and the Great Recession

In the late 2000s, many giant U.S. corporations were on the verge of collapse as a result of years of risky investing, accounting blunders, and inefficient operations. These corporations, such as Bear Stearns , American International Group (AIG), and others, employed thousands of workers and contributed billions of dollars to the country's economy. This time period is now known as The Great Recession , and the U.S. was in the throes of a deep global recession .

While many executives of these companies blamed the poor state of the economy for the financial troubles their businesses were experiencing, in actuality, the greater economic recession simply exposed the risky behaviors that they had been engaging in for many, many years.

Ultimately, the U.S. government deemed these companies too big to fail and came to their rescue in the form of a bailout. This bailout cost taxpayers hundreds of billions of dollars. The U.S. government reasoned that allowing businesses to fail that were so crucial to the status quo of the country's economy could threaten to push the U.S. into a deeper economic depression from which it ultimately might not recover.

These bailouts—executed at the expense of taxpayers—presented a huge moral hazard situation; the willingness of the government to bail out their companies sent a message to executives at large corporations that any economic costs from engaging in excessively risky business activities (in order to increase their profits) would be shouldered by someone other than themselves.

The Dodd-Frank Act of 2010 attempted to mitigate the likelihood of another moral hazard situation involving these "too-big-to-fail" corporations. The Act forced these corporations to create specific plans in advance for how to proceed if they got into financial trouble again. The Act also stipulated these companies would not be bailed out at the expense of taxpayers again in the future.

Moral Hazard in Salesperson Compensation

The compensation method for how some salespeople are paid represents another situation where moral hazard is more likely to occur. When a business owner pays a salesperson a set salary—not based on their performance or sales numbers—that salesperson may have an incentive to put forth less effort, take longer breaks, and generally have less motivation to increase their sales numbers than if their compensation was tied to their sales numbers.

In this scenario, it can be said that the salesperson is acting in bad faith if they are not doing the job they were hired to do to the best of their ability. However, the salesperson knows the consequences of this decision (potentially lower revenues ) will be shouldered by the management of the company or the business owner, while their individual compensation will not be impacted.

For this reason, most companies choose to pay only a smaller, base pay salary to their salesforce, with the majority of their compensation coming from commissions and bonuses that are directly tied to their sales numbers. This compensation style may provide salespeople with a greater incentive to work harder because they will bear the cost of any missed sales opportunities in the form of lower paychecks.

Moral Hazard in Insurance

Moral hazard is often associated with the insurance industry. Insurance companies fear that individuals may engage in more risky behavior because they are not concerned with the costs associated with damages that may arise from that risky behavior as the costs are covered by the insurance company.

For example, a car driver may drive faster knowing that the damage on their car will be covered by the insurance company if they get in an accident. Similarly, a homeowner that smokes in bed may be less concerned if a fire breaks out causing damages because they have homeowners insurance that includes fire coverage that would cover the costs.

Moral hazard only applies once an individual has insurance coverage, not before. Adverse selection is the term used when individuals are deciding on how much and the type of insurance to purchase based on their own risky behavior.

Moral hazard is an issue for insurance companies because when insured customers have a relaxed attitude about their own risk, it can result in insurance companies paying out more insurance claims.

Why Is Moral Hazard an Economic Problem?

You can look at the 2008 financial crisis to see that moral hazard is an economic problem because it leads to an inefficient allocation of resources. It does so because one party imposes a larger cost on another party, which can result in significantly high costs to an economy if done on a macro scale.

What Is the Moral Hazard Problem?

The moral hazard problem is when one party in a deal or transaction is more comfortable taking risks, whether physical or financial, than they otherwise would have been because they know that they will not be responsible for negative consequences.

Why Is It Called Moral Hazard?

It is called "moral hazard" because morality comes into play in determining parties' right and wrong behavior in a transaction that could lead to or prevent a hazard whereby the party not engaging in the behavior will possibly suffer the consequences.

Why Is It Important for a Business to Anticipate Moral Hazard?

Moral hazard is an economic cost, so it is important for businesses to anticipate these costs. It is best seen through the insurance industry: Insurance companies need to be aware that the behavior of individuals is likely to be riskier if they are insured, so the likelihood of accidents and paying out claims increases. Insurance providers will need to factor moral hazard into their overall financial plan, anticipating revenues, costs, and profits.

Moral hazard in business can lead to some parties making more reckless or imprudent decisions than they otherwise would because they won't be the ones to bear the risk. Some examples include the bank bailouts of the Great Recession, salesperson compensation, and insurance. Companies should anticipate problems arising from moral hazard so that they don't detract from their bottom line.

Federal Reserve History. " Support for Specific Institutions ."

Lucas, Deborah. " Measuring the Cost of Bailouts ." Annual Review of Financial Economics , vol. 11, December 2019, pp 85-108.

St. Louis Federal Reserve. " Dodd-Frank Wall Street Reform and Consumer Protection Act ."

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