Analysis of Individual Mandate

A requirement that individuals have health insurance, either through an employer, an individual plan, a purchasing pool, or by enrolling in a public insurance program (such as Medicaid).

These are the nine performance dimensions against which we measured Individual Mandate:

Spending

An individual mandate will have a negligible effect on aggregate national health spending but will increase government spending on Medicaid and premium subsidies:

  • Aggregate national health spending will increase by $7 billion to $26 billion, depending on the design of the mandate; this represents an increase of 0.3-1.2 percent of total spending and is indistinguishable from zero. Read more below
  • Under an individual mandate, Medicaid expenditures would increase by up to $25 billion (about 7.6 percent), and overall government spending in health care would increase $12 billion to $62 billion (1.2–6 percent), depending on the design of the mandate Read more below
  • Government cost per newly insured person is similar at all nonzero subsidy levels and declines as the size of the penalty increases. Read more below
  • In general, other researchers who have used microsimulation methods to estimate the effect of an individual mandate on spending have estimated substantially larger increases than we did. Read more below

We modeled an individual mandate with the following design:

  • All individuals without exception are required to obtain health insurance coverage.
  • A national purchasing pool is created, with private insurers offering coverage that complies with federal rating regulations.
  • Plans participating in the national purchasing pool agree to sell policies to all who apply (i.e., guaranteed issue required for participation).
  • Premiums in the purchasing pool can vary only with age, and four rate bands were created (0–17, 18–29, 30–49, 50–64 years).
  • We modeled an average plan for the purchasing pool using an actuarial value of 0.70, which corresponds to a plan that is slightly more generous than a typical non-group plan and less generous than a typical employer plan.
  • We estimated premiums for plans in the purchasing pool in our model using the experience of enrollees; they range from approximately $1,000–$1,400 (for enrollees under age 17) to $4,400–$6,200 (for enrollees ages 50 to 64).1
  • The administrative cost of the purchasing pool in the results presented is 15 percent, but we also tested administrative costs of 25 percent.
  • Subsidies for the purchase of insurance are available only for policies from the purchasing pool. We tested a variety of subsidy structures related to income. Full subsidies are available for people whose income falls below a minimum threshold (we use 100 percent of the federal poverty level [FPL] in the results presented, but we also tested 150 percent of FPL). Partial subsidies are available on a linear decreasing sliding scale, up to a maximum income threshold (we tested 150 percent, 200 percent, 250 percent, 300 percent, and 400 percent of the FPL).
  • Individuals who are Medicaid eligible and individuals with access to employer sponsored insurance (either their own employer or that of a spouse) cannot buy into the purchasing pool. This follows the structure of the individual mandate in the Massachusetts Health Reform Law of 2006.
  • Penalties for failure to purchase insurance are calculated as a percentage of the premium an individual would have paid in the purchasing pool. We tested four penalty levels: 0 percent, 30 percent, 50 percent, and 80 percent.

We made the following assumptions in modeling an individual mandate:

  • The penalty is perfectly enforced. This assumption can be relaxed in future iterations of the model.
  • Households2 in which all members are fully insured through employer sponsored insurance make no change in response to the mandate.
  • Households with at least one uninsured member make decisions about how to respond to the individual mandate, to the penalty for noncompliance, and to the availability of subsidies, if applicable, by choosing the option that provides the greatest value for the money (i.e., utility maximization). The methods are described in greater detail in the overview of the microsimulation model.
  • We include in the model a parameter that accounts for the apparent disutility of insurance held by those who are currently eligible for Medicaid or employer sponsored insurance and who have elected not to participate.

Aggregate national health spending will increase by $7 billion to $26 billion, depending on the design of the mandate; this represents an increase of 0.3–1.2 percent of total spending and is indistinguishable from zero.

An individual mandate is not likely to substantially increase National Health Expenditures. We tested a large number of scenarios in which we varied the subsidy schedule and penalty levels. Figure 1 shows the effect of different subsidy levels and penalty sizes on national health spending (in 2007 dollars). The choice of subsidy level has the greatest effect on increases in national health spending, but none of the scenarios shown here (which reflect the range of what we tested) is likely to result in significant increases in spending.

 Effect of Subsidy Levels and Size of Penalty on Changes in Aggregate National Health Spending SOURCE: RAND COMPARE microsimulation modeling results, December 28 2008 and based on 2007 dollars and spending levels.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the legend (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

Under an individual mandate, Medicaid expenditures would increase by up to $25 billion (about 7.6 percent) and overall government spending on health care would increase $12 billion to $62 billion (1.2–6 percent), depending on the design of the mandate.

An individual mandate will result in an increase in government spending of about 1–6 percent, as shown in Table 1. Because the subsidy cost is all borne by government, as the size of the subsidy increases, so will government spending. Medicaid increases are insensitive to the size of the subsidy, since people who are eligible for Medicaid cannot take advantage of the subsidy. For Medicaid, the increase in spending is determined by the size of the penalty and ranges from no change without a penalty to about a $25 billion increase (7.6 percent of total Medicaid spending in 2007) at a penalty of 80 percent.

Table 1.
Changes in Spending Under an Individual Mandate,
by Category of Spending and Level of Subsidy Change in Spending, by Category
Change in Spending, by Category Penalty (as a Percentage of the Premium in the Purchasing Pool) Level of Subsidy
No Subsidy Full Subsidy for Persons
Below 100% FPL Plus:
Partial Subsidy for Households up to 200% FPL Partial Subsidy for Households up to 300% FPL Partial Subsidy for Households up to 400% FPL
National Health Expenditures ($ billions) 30 10.9 14.6 16.3 17.7
50 13.3 18.1 20.1 21.3
80 18.2 22.2 24.6 25.8
 
Medicaid ($ billions)* 30 12.2 12.5 12.5 12.5
50 17.4 17.6 17.7 17.7
80 24.3 24.5 24.8 24.8
 
Government ($ billions)** 30 12.2 35.8 40.1 47.4
50 17.4 42.1 48.1 54.4
80 24.3 49.5 56.0 62.5

SOURCE: RAND COMPARE microsimulation modeling results, December 28 2008, and based on 2007 dollars and spending levels.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the table (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.
* The Medicaid cost changes slightly as a function of subsidy generosity because the penalty is a function of the premium in the purchasing pool. If the penalty were a fixed amount, these numbers would be constant across the columns.
** Government spending is equal to Medicaid plus the cost of the subsidy.

Government cost per newly insured is similar at all subsidy levels and declines as the size of the penalty increases.

We examined the public cost (subsidies plus Medicaid spending) of expanding coverage under an individual mandate. Table 2 shows the pattern we observed across all scenarios with a subsidy option. The government cost per newly insured falls as the income level at which people are eligible for a subsidy increases, because persons who become newly insured at lower penalty levels are more likely to be enrolling in Medicaid or taking government subsidies; newly insured persons at higher penalty levels are less likely to be taking subsidies or are younger and healthier and have lower premiums.

Table 2.
Government Cost per Net Newly Insured Under an Individual Mandate
for a Selected Subsidy* Under Different Noncompliance Penalties
  Size of Penalty (Percentage of Premium)
No penalty 30% 50% 80%
Government cost per newly insured person $2,655 $2,284 $2,110 $1,835

SOURCE: RAND COMPARE microsimulation modeling results, December 28 2008, and based on 2007 dollars and spending levels.
*The subsidy selected is one in which households under 100 percent FPL are eligible for a full premium subsidy; households up to 400 percent FPL are eligible for a partial subsidy, which is calculated on a sliding scale between 100 percent FPL and 400 percent FPL.

In general, other researchers who have used microsimulation methods to estimate the effect of an individual mandate on spending have estimated substantially larger increases than we did.

Several studies have estimated the effect that expanding coverage through an individual mandate would have on spending. Most include an individual mandate, along with other policy options to increase coverage and decrease individual financial risk. Because of differences in assumptions about the design of the individual mandate, it is somewhat difficult to compare estimates across studies.

Gruber (2008) simulated an individual health insurance mandate combined with subsidies for individuals up to 400 percent of FPL and a voucher targeted at people who have employer sponsored coverage available to them but who have not obtained insurance. He estimated that such a policy option would increase coverage among the uninsured by 97 percent, at a cost of $2,700 per net newly insured individual, and a total government cost of $124 billion. It is difficult to compare Gruber's result with ours because he assumes that, if the penalty were strong enough, coverage among the uninsured would increase by 97 percent, whereas our coverage estimate depends on the value we choose for the noncompliance penalty. In addition, his estimates include the effect of vouchers for uptake of employer sponsored insurance, which we do not model. For our model to achieve the high level of coverage that Gruber assumed, we would need to consider a high penalty and subsidies available for persons with higher incomes. For example, with an 80 percent penalty and households up to 400 percent FPL eligible for a subsidy, we estimate that the cost per net newly insured person would be $1,835, for a total cost of $62.5 billion and a reduction in the number of the uninsured of 76 percent. Contributing to the low cost per newly insured person in our model is the fact that people with access to employer sponsored insurance are not eligible for the subsidy, so all the newly insured people with group coverage do not cost the government anything. Another contributing factor is the relatively low administrative cost we assumed for the purchasing pool (15 percent): If we increase it to 25 percent, the cost per newly insured person increases by $190–$550 but the pattern of results does not change.

Lambrew and Gruber (2006–2007) estimated that an individual mandate, combined with Medicaid expansion, tax credits, and a purchasing pool, would increase overall spending by $56 billion to $114 billion (in 2003 dollars), depending on assumptions about the size of the Medicaid expansion and tax credits. Our scenario of a generous subsidy with a medium to high penalty falls on the lower end of their estimates. They also found under their design that an individual mandate would cover everyone; our estimates of increased coverage and the effect on national health spending are much smaller, since our policy change is much more restrictive than the one they modeled.

Blumberg et al. (2006) created several policy change scenarios in which they considered an individual mandate alone and in combination with an employer mandate, both with provisions that limit expenses for low income individuals. Their model focused exclusively on the state of Massachusetts. They estimated that, under an individual mandate alone, state government spending would increase by $2.03 billion (60.8 percent), employer spending would increase by $209 million (2.2 percent), and individual/household spending would increase by $78 million (0.8 percent). Under an individual mandate plus an employer mandate, state government spending would increase by $2.23 billion (66.7 percent), employer spending would increase by $595 million (7.9 percent), and individual/household spending would decrease by $231 million (–2.4 percent).

Holahan, Winterbottom, and Zedlewski (1994–1995) projected health spending under an individual mandate that included low–income subsidies, tax deductibility of premium payments, and a set of assumptions about employers' coverage decisions. They estimated that aggregate national health spending (in 1998 dollars) would increase by $47.3 billion to $54.1 billion (10–13 percent), depending on the scenario. They estimated that government spending would increase by $40.4 billion to $80.9 billion (131–262 percent), employer spending would decrease by $37 billion to $208.8 billion (18–100 percent), and individual/household spending would increase by $29.2 billion to $205.2 billion (16–113 percent). Note that Blumberg et al. predict a small increase in employer spending under the individual mandate, whereas Holahan, Winterbottom, and Zedlewski predict a decline. The difference is that the Blumberg study allows firms to begin offering insurance after the mandate, in response to worker demand. Holahan, Winterbottom, and Zedlewski hold offers constant in their first scenario and assume that all firms drop insurance in their second scenario.

Tobin (1994) projected health spending in 1999 under an individual mandate that included low–income subsidies and eliminated the tax exemption for employers who provide group insurance. He estimated that government spending in 1999 would increase to between $153 billion and $313 billion, depending on the income threshold for full subsidization.

  1. Because premiums are determined endogenously, the specific value varies in each modeled scenario. For example, realized premiums are higher in scenarios in which the penalty for noncompliance with the mandate is low, since some healthy people will opt to pay the penalty rather than obtaining insurance in the purchasing pool.
  2. By households, we technically mean health insurance eligibility units (HIEUs); that is, the group of people who, together, are eligible for private and/or public coverage.

References

Blumberg LJ, Holahan J, Weil A, Clemans-Cope L, Buettgens M, Blavin F, Zuckerman S, "Toward Universal Coverage in Massachusetts," Inquiry, Vol. 43, No. 2, Summer 2006, pp. 102–121.

Gruber J, "Covering the Uninsured in the U.S.," Cambridge, Mass.: National Bureau of Economic Research, Working Paper No. 13758, January 2008. As of March 4, 2009:
http://www.nber.org/papers/w13758

Holahan J, Winterbottom C, Zedlewski S, "The Distributional Effects of Employer and Individual Health Insurance Mandates," Inquiry, Vol. 31, No. 4, Winter 1994–1995, pp. 368–384.

Lambrew JM, Gruber J, "Money and Mandates: Relative Effects of Key Policy Levers in Expanding Health Insurance Coverage to All Americans," Inquiry, Vol. 43, No. 4, Winter 2006–2007, pp. 333–344.

Tobin J, "Health Care Reform as Seen by a General Economist," New Haven, Conn.: Yale University, Cowles Foundation, Discussion Paper No. 1073, May 1994.

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Consumer Financial Risk

An individual mandate will have a negligible effect on consumer financial risk for the non-elderly, but it will increase the median spending on health care by the newly insured:

  • Based on our model, an individual mandate results in no discernible change in consumer financial risk for the non-elderly overall. Read more below
  • Based on our model, the median proportion of income spent on health care increases substantially among those who become newly insured under an individual mandate. Read more below
  • Based on our model, the proportion of households that spend more than 10 percent of income on health care increases at all subsidy levels among those who become newly insured under an individual mandate. Read more below
  • Subsidies are a prominent consideration among other researchers who have modeled individual mandates. Read more below

Based on our model results, an individual mandate results in no discernible change in consumer financial risk for the non-elderly overall.

An individual mandate does not noticeably change the median proportion of income that the non–elderly spend on health care. Before and after the policy change, about half of the non–elderly spend more than 6 percent of their income on health care.

To evaluate whether the policy change was affecting the number of people facing excess spending on health care, we also estimated the proportion of non-elderly households that spends more than 10 percent of their income on health care. Although there is no agreed upon definition of what constitutes excess spending, Schoen and colleagues (2005) have proposed 10 percent as a level at which spending on health care might threaten the ability of a household to meet basic needs, such as food, shelter, and transportation. We found very little difference in the proportion of households with high levels of spending on health care before and after the policy change. About 27 percent of the non-elderly would have health care expenses in excess of 10 percent of income before an individual mandate is implemented. After the individual mandate, that proportion increases slightly in all scenarios; the largest change (from 27 percent before the mandate to 30 percent after the mandate, a 12 percent increase) is in the scenario with no subsidy and an 80 percent penalty for noncompliance.

Based on our model, among those who become newly insured under an individual mandate, the median proportion of income spent on health care increases substantially.

This finding is counterintuitive for many people, but it reflects the fact that the uninsured use fewer health services than the rest of the population and may not always pay the full cost of those services. By acquiring private insurance, they will begin paying premiums and increase their use of services (and thus pay deductibles and copayments). By design, they will be spending more money on health care than they did before.

As shown in Table 1, before an individual mandate is implemented, the median proportion of income spent on health care among those without insurance who will take up insurance in response to the mandate is 2–3 percent. For an individual with an income at 100 percent of the federal poverty level (FPL) ($10,400 in 2008), this percentage represents $208–$312. This is markedly lower than the median among the general non–elderly population and reflects the lower utilization of health care services among the uninsured, as well as the fact that a portion of the uninsured are young and in good health. After the mandate is introduced, the median proportion of income spent on health care increases by 65 percent to nearly 300 percent. The percentage increase is lower at higher subsidy levels; the biggest impact occurs in the scenario with no subsidy available and a penalty of 80 percent.

Table 1.
Median Proportion of Income Spent on Health Care,
Among the Newly Insured, Before and After Implementing
an Individual Mandate, by Subsidy Level
Median Percentage of Income Spent on Health Care Penalty (as a Percentage of the Premium in the Purchasing Pool)
No penalty 30% 50% 80%
No Subsidy  
Before mandate   2.4 2.2 2.2
After mandate   8.3 7.7 8.7
Partial Premium Subsidy up to 200% FPL  
Before mandate 3.1 3.1 2.8 2.5
After mandate 6.1 6.2 6.3 6.7
Partial Premium Subsidy up to 300% FPL  
Before mandate 3.3 3.1 2.8 2.8
After mandate 5.7 5.9 6.0 6.2
Partial Premium Subsidy up to 400% FPL  
Before mandate 3.2 3.1 2.9 3.0
After mandate 5.3 5.4 5.5 6.0

SOURCE: RAND COMPARE modeling estimates, December 28 2008, and based on 2007 dollars and spending levels.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the table (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

Based on our model, the proportion of households that spends more than 10 percent of income on health care increases at all subsidy levels among those who become newly insured under an individual mandate.

We observe from our modeling results, across all scenarios, an increase in the proportion of households that spends more than 10 percent of their income on health care after implementation of an individual mandate. The proportion with high levels of spending increases as the penalty gets larger, a pattern that is consistent across all subsidy levels. Before the mandate, among those who will become newly insured after the policy change, about 18–25 percent of households spend more than 10 percent of their income on health care. For a family of four at 100 percent FPL, this would be $2,120, which is lower than the mean spending for the non–elderly with private insurance ($3,165). This range is lower than what we found in the general non–elderly population, in which 27 percent of households have high levels of spending relative to income. After the mandate, the proportion increases to 28–45 percent, depending on the level of subsidy and the size of the penalty for noncompliance. The largest increases occur in the scenario with no subsidy. In the scenario with the highest penalty size and partial premium subsidies available for persons up to 200 percent FPL, after the mandate 34.5 percent of households will spend more than 10 percent on health care, a 65 percent increase. This finding underscores the reason that most policymakers include a subsidy schedule with an individual mandate.

Table 2.
Change in the Proportion of Newly Insured Households
Spending More than 10 percent on Health Care,
Before and After an Individual Mandate
Percentage of Households Spending More Than 10% of Income on Health Care Penalty (as a Percentage of the Premium in the Purchasing Pool)
No penalty 30% 50% 80%
No Subsidy  
Before mandate   17.5 17.8 19.8
After mandate   42.3 39.8 45.3
Partial Premium Subsidy up to 200% FPL  
Before mandate 24.0 22.4 22.1 21.3
After mandate 35.0 32.3 33.4 34.5
Partial Premium Subsidy up to 300% FPL  
Before mandate 24.5 22.5 21.7 21.6
After mandate 31.0 29.7 30.9 31.2
Partial Premium Subsidy up to 400% FPL  
Before mandate 24.5 22.2 22.0 21.6
After mandate 28.8 28.1 29.2 29.2

SOURCE: RAND COMPARE microsimulation modeling results, December 28 2008, and based on 2007 dollars and spending levels.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the table (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

Subsidies are a prominent consideration among other researchers who have modeled individual mandates.

Lambrew and Gruber (2006–2007) argue that some families could end up spending as much as 20 percent of their income on health insurance premiums under a mandate with modest subsidies. However, if the mandate were coupled with significant subsidies, this problem would be mitigated.

Holahan, Winterbottom, and Zedlewski (1994–1995) modeled health spending across the distribution of income and predicted that "net benefits" (defined as the total value of health benefits received minus total payments, including lost wages, premium contributions, and taxes) are positive for families in the bottom 40 percent of the income distribution under individual mandates. Although families in the middle quintile have negative net benefits, they are better off under the policy change than under the current system. Individuals in the top 40 percent of the income distribution, who have negative net benefits both before and after the change, are slightly worse off with individual mandates.

References

Holahan J, Winterbottom C, Zedlewski S, "The Distributional Effects of Employer and Individual Health Insurance Mandates," Inquiry, Vol. 31, No. 4, Winter 1994–1995, pp. 368–384.

Lambrew JM, Gruber J, "Money and Mandates: Relative Effects of Key Policy Levers in Expanding Health Insurance Coverage to All Americans," Inquiry, Vol. 43, No. 4, Winter 2006–2007, pp. 333–344.

Schoen C, Doty MM, Collins SR, Holmgren AL, "Insured But Not Protected: How Many Adults Are Underinsured?" Health Affairs, Web Exclusive, July 14, 2005, pp. w5-289–w5-302.

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Waste

We do not know whether increases in coverage will lead to decreases in waste, because there is little direct evidence and because the theoretical relationships may have offsetting effects:

  • No studies directly examine the effect of increasing coverage on waste. Read more below
  • Clinical waste could decrease if the newly insured shift their patterns of utilization from less efficient to more efficient providers, particularly if they shift from using emergency departments for primary care to visiting physicians' offices. Read more below
  • Administrative waste could increase if a significantly greater number of eligible persons are subjected to complex requirements needed to qualify for subsidies or to avoid penalties, and it could decrease to reflect reductions in uncompensated care. Read more below
  • Operational waste is unlikely to be affected, because the policy change does not have a direct effect on the way the delivery system is organized. Read more below

No studies directly examine the effect of increasing coverage on waste.

We conceptualize waste in the U.S. health care system as a measure of inefficiency and categorize waste as administrative, operational, or clinical (Bentley et al., 2008). Waste can be identified at the system level and at the individual level, although substantial challenges remain in estimating the magnitude of waste.

No studies explicitly examine the effect of significant coverage expansions on waste or evaluate the effect of becoming uninsured on the likelihood that both appropriate and inappropriate care will be received. Although considerable literature shows that increased cost sharing reduces the use of health services (Newhouse and Insurance Experiment Group, 1993; Wharam et al., 2007; Keeler et al., 1996), it can be difficult to determine whether the services that were eliminated were wasteful. Most studies find that cost sharing leads to reductions in both necessary and unnecessary care. The studies have, however, focused more on what happens when the cost of care increases than what happens when it declines.

Some studies evaluate the effect of coverage expansions on costs of care (and, potentially, waste), focusing on the relative efficiency of the settings in which patients receive care. Several studies support the notion that the uninsured tend to get care in costly settings, such as the emergency department (ED). Walls, Rhodes, and Kennedy (2002) found that lack of insurance predicted ED use as a patient's usual source of care, whereas Begley and colleagues (2006) found that primary care-related ED use was strongly associated with lack of insurance and poverty. Other studies have pointed out that, whereas the uninsured may use the ED more for primary care, their use of the ED in terms of number of visits is not greater overall than that of the insured (Hunt, 2006; Cunningham, 2006b; Weber et al., 2005; Cunningham, 2006a). Studies also suggest that, if an uninsured person gets access to insurance, inappropriate ED use may decrease. For example, Cunningham and Hadley (2004) found that communities with higher rates of insurance coverage had lower use of hospital EDs, and Cunningham (2006a) found that a decrease in Medicaid/State Children's Health Insurance Program (SCHIP) enrollment would lead to an increase in ED visits by the uninsured.

Clinical waste could decrease if newly eligible enrollees shift their patterns of utilization from less efficient to more efficient providers, particularly if they shift from using emergency departments for primary care to visiting physicians' offices.

The newly insured under an individual mandate will be using a health care delivery system that is believed to be replete with waste. Although the uninsured may be subject to higher risk of clinical waste in some areas, such as using expensive settings to obtain primary care, they may be shielded from clinical waste in other areas, such as inappropriate use of procedures, medications, and other interventions. Thus, expanding coverage to the uninsured could decrease clinical waste by shifting some primary care services from EDs, which are inherently inefficient providers of primary care services, to outpatient settings. The degree to which waste is reduced will depend on the magnitude of such changes in patterns of service delivery. On the other hand, we estimate that adults who were uninsured will increase their utilization of health services by 35–62 percent; some of the increase will be of low value—that is, it will be unlikely to produce any health benefit—which will increase waste.

Administrative waste could increase if a significantly greater number of eligible persons are subjected to complex requirements needed to qualify for subsidies or to avoid penalties, and it could decrease to reflect reductions in uncompensated care.

This policy change may have some effect on administrative waste as well, but the net change is not clear. Administrative waste may be increased if the requirements to qualify for premium subsidies or to avoid penalties are complex. Depending on the system that is used for verifying coverage, monitoring compliance, and collecting penalties, administrative waste could be increased. On the other hand, some portion of administrative overhead currently is allocated to pay for care that would otherwise be uncompensated; this portion would decrease as previously uninsured individuals acquire insurance. Although administrative overhead is not waste per se, if the policy option reduces the amount of uncompensated care provided by an institution, then waste may decrease. It is difficult at this time to quantify the magnitude of potential increases or decreases in these factors.

Operational waste is unlikely to be affected, because the policy change does not have a direct effect on the way the delivery system is organized.

Operational waste refers to the inefficiency with which services are delivered within organizations. We do not expect that this policy change will directly affect the way organizations operate, because the effect of the increase in the number of those who are insured on any one organization will be quite small and is unlikely to motivate significant changes in the way care is organized and delivered. This option does not include changes in payment rules or other delivery system changes that might be necessary to stimulate significant reductions in operational waste.

References

Begley CE, Vojvodic, RW, Seo M, Burau K, "Emergency Use and Access to Primary Care: Evidence from Houston, Texas," Journal of Health Care for the Poor and Underserved, Vol. 17, No. 3, August 2006, pp. 610–624

Bentley TGK, Effros RM, Palar K, Keeler EB, "Waste in the U.S. Health Care System: A Conceptual Framework," Milbank Quarterly, [Epub November 21 2008], Vol. 86, No. 4, December 2008, pp. 629–659.

Cunningham PJ, "Medicaid/SCHIP Cuts and Hospital Emergency Department Use," Health Affairs, Vol. 25, No. 1, January/February, 2006a, pp. 237–247.

Cunningham PJ, "What Accounts for Differences in the Use of Hospital Emergency Departments Across U.S. Communities?" Health Affairs, Web Exclusives, [Epub July 18 2006], Vol. 25, No. 5, September/October 2006b, pp. w324–w336.

Cunningham P, Hadley J, "Expanding Care Versus Expanding Coverage: How to Improve Access to Care," Health Affairs, Vol. 23, No. 4, July/August 2004, pp. 234–244.

Cunningham PJ, May JH, Insured Americans Drive Surge in Emergency Department Visits, Washington, D.C.: Center for Studying Health Systems Change, Issue Brief No. 70, October 2003, pp. 1–6.

Hunt KA, "Characteristics of Frequent Users of Emergency Departments," Annals of Emergency Medicine, [Epub March 30 2006], Vol. 48, No. 1, July 2006, pp. 1–8

Keeler EB, Malkin JD, Goldman DP, Buchanan JL "Can Medical Savings Accounts for the Nonelderly Reduce Health Care Costs?" The Journal of the American Medical Association, Vol. 275, No. 21, June 5 1996, pp. 1666–1671.

Newhouse JP, Insurance Experiment Group, Free for All? Lessons from the RAND Health Insurance Experiment, Cambridge, Mass.: Harvard University Press, 1993.

Walls CA, Rhodes KV, Kennedy JJ, "The Emergency Department as Usual Source of Medical Care: Estimates from the 1998 National Health Interview Survey," Academic Emergency Medicine, Vol. 9, No. 11, November 2002, pp. 1140–1145.

Weber EJ, Showstack JA, Hunt KA, Colby DC, Callaham ML, "Does Lack of a Usual Source of Care or Health Insurance Increase the Likelihood of an Emergency Department Visit? Results of a National Population Based Study," Annals of Emergency Medicine, [Epub October 24 2004], Vol. 45, No. 1, January 2005, pp. 4–12.

Wharam JF, London BE, Galbraith AA, Kleinman KP, Soumerai SB, Ross-Degnan D, "Emergency Department Use and Subsequent Hospitalizations Among Members of a High–Deductible Health Plan," The Journal of the American Medical Association, Vol. 297, No. 10, March 14, 2007, pp. 1093–1102.

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Reliability

An individual mandate will have no noticeable effect on the reliability of health care delivery at the system level:

  • At the system level, there is no clear empirical evidence of a relationship between coverage and reliability. Read more below
  • For newly insured individuals, the reliability with which they receive certain services may increase, but they may also receive services they do not need. Read more below

At the system level, there is no clear empirical evidence of a relationship between coverage and reliability.

There are two ways to consider the effect of an individual mandate on reliability—that is, on the likelihood that patients will receive necessary care. The first is whether the policy change directly affects the way the health delivery system functions. The second is whether individuals who become newly insured are more likely to receive appropriate services.

From the system perspective, we have no evidence to suggest that an individual mandate will affect the way health services are delivered. Our estimates suggest that about 11–34 million people would newly acquire insurance under an individual mandate; this represents 4–11 percent of the U.S. population. To see a system level effect on reliability, we would have to assume that the newly insured are currently receiving no needed services and we would have to further assume that, with insurance, they would receive perfect care. Neither of these assumptions is supported by the literature; thus, we anticipate that the aggregate effect on reliability will be negligible.

Further, there is no evidence that suggests that coverage expansions alone will motivate systems to improve reliability. Such an improvement represents a second order effect. Reliability improvements are more likely to be observed in response to policy changes that are directly aimed at motivating systems to make changes in the way they deliver care.

For newly insured individuals, the reliability with which they receive certain services may increase, but they may also receive services they do not need.

For individuals who obtain insurance under this policy, the likelihood that they will receive appropriate care may increase, although the evidence for this possibility is mixed. Some evidence suggests that health insurance improves access to care and quality of care. Several studies have found substantial differences in quality measures between uninsured and insured populations (Ayanian et al., 2000; Ross, Bradley, and Busch, 2006). Ayanian et al. (2000) found that uninsured adults were less likely to see a physician when needed. Ross, Bradley, and Busch (2006) found that the uninsured used fewer recommended health services. Buchmueller et al.'s (2005) review suggests that utilization for all types of services increases when coverage is expanded. There is also evidence that expanding insurance increases the total number of hospitalizations for children but reduces avoidable hospitalizations (Dafny and Gruber, 2000).

For those services for which access is the primary determinant of the likelihood that appropriate care will be delivered, such as screening or preventive care, improved access may increase the reliability of care for the individual. However, strong evidence suggests that the health care system has significant deficiencies experienced by everyone who seeks health care, and there is no reason to believe that newly insured individuals will not experience those same deficiencies. The most comprehensive study of quality in the United States found that, among those with at least one visit in two years, the uninsured and insured were both receiving about half of recommended care for the leading causes of death and disability (Asch et al., 2006). Leape et al. (1999) found no overall differences in underuse of coronary revascularization between uninsured and insured people. Young et al. (2001) found that "insurance and income had no effect on receipt of appropriate care for depressive and anxiety disorders." Harman, Edlund, and Fortney (2004) found disparities in initiation of depression treatment between the uninsured and the insured, but no differences in quality of care once treatment was initiated.

We do not know how the reliability of care for individuals who were previously uninsured will change when they acquire insurance; reliability will depend on their health care needs, their own care seeking behavior before and after the change in insurance status, and the quality of care delivered in the places they go for care.

References

Asch SM, Kerr EA, Keesey J, Adams JL, Setodji CM, Malik S, McGlynn EA, "Who Is at Greatest Risk for Receiving Poor–Quality Health Care? The New England Journal of Medicine, Vol. 354, No. 11, March 16, 2006, pp. 1147–1156.

Ayanian JZ, Weissman JS, Schneider EC, Ginsburg JA, Zaslavsky AM, "Unmet Health Needs of Uninsured Adults in the United States," The Journal of the American Medical Association, Vol. 284, No. 16, October 25, 2000, pp. 2061–2069.

Buchmueller TC, Grumbach K, Kronick R, Kahn JG, "The Effect of Health Insurance on Medical Care Utilization and Implications for Insurance Expansion: A Review of the Literature," Medical Care Research and Review, Vol. 62, No. 1, February 1 2005, pp. 3–30.

Dafny L, Gruber J, "Does Public Insurance Improve the Efficiency of Medical Care? Medicaid Expansions and Child Hospitalizations," Cambridge, Mass.: National Bureau of Economic Research, Working Paper 7555, February 2000. As of March 5, 2009: http://www.nber.org/papers/w7555.

Harman JS, Edlund MJ, Fortney JC, "Disparities in the Adequacy of Depression Treatment in the United States," Psychiatric Services, Vol. 55, No. 12, December 2004, pp. 1379–1385.

Leape LL, Hilborne LH, Bell R, Kamberg C, Brook RH, "Underuse of Cardiac Procedures: Do Women, Ethnic Minorities, and the Uninsured Fail to Receive Needed Revascularization?" Annals of Internal Medicine, Vol. 130, No. 3, February 2, 1999, pp. 183–192.

Ross JS, Bradley EH, Busch SH, "Use of Health Care Services by Lower–Income and Higher–Income Uninsured Adults," The Journal of the American Medical Association, Vol. 295, No. 17, May 3, 2006, pp. 2027–2036.

Young AS, Klap R, Sherbourne CD, Wells KB, "The Quality of Care for Depressive and Anxiety Disorders in the United States," Archives of General Psychiatry, Vol. 58, January 2001, pp. 55–61.

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Patient Experience

No studies directly examine the link between an employer mandate and changes in patient experience:

  • No empirical studies directly analyze the relationship between an individual mandate and changes in patient experience. Read more below
  • Generalizations from cross–sectional studies suggest that the patient experience of formerly uninsured individuals will improve if those individuals acquire insurance. Read more below

No empirical studies directly analyze the relationship between an individual mandate and changes in patient experience.

Systematic data about patient experiences among the uninsured are scarce: Most of the routine measurements of patient experience are conducted among those with insurance (private, Medicaid, or Medicare). We also do not know how patients' experiences change when they go from being uninsured to being insured. Nor do we know whether patients who newly acquire insurance are likely to change the places they go for care and thus have a different experience than when they were uninsured. Even under the most effective scenario, the newly insured would represent just 11 percent of the population, which might not result in any significant aggregate differences in patient experience after the policy change.

Generalizations from cross–sectional studies suggest that the patient experience of formerly uninsured individuals will improve if those individuals acquire insurance.

The uninsured population encounters considerable difficulties in both access to and continuity of care. To the extent that this policy option would allow people to move from being uninsured to having insurance, we expect that the patient experience will improve as people have greater access to care and may be able to develop a usual source of care. Those who had coverage prior to the policy change may not see any change in patient experience.

The effect of this policy on patient experience may be somewhat tempered by the ability of the newly insured to obtain care. Some of the individuals who obtain insurance under the policy we modeled have lower incomes and may live and work in areas with limited capacity. The policy option could result in people obtaining insurance but not gaining better access to care, resulting in a poor patient experience.

Information from the existing literature provides some evidence that the newly insured might have a better experience with care. Multiple studies confirm that people without insurance have more negative experiences of care. Schoen and DesRoches (2000) compared the experience of the continuously insured population with that of the uninsured and discontinuously insured. They found that those individuals who experienced a gap in coverage and uninsured individuals were at higher risk of going without needed care and of having problems paying medical bills, and that they rated care more negatively than those with continuous insurance. Schoen et al. (1997) compared low income uninsured adults with those with public or private coverage and found that the uninsured were less likely to have a regular provider and rated care more negatively than those with insurance. Newacheck, Hughes, and Stoddard (1996) found that uninsured children were twice as likely to lack a usual source of care and twice as likely to wait 60 minutes or more for a health care visit as were insured children. Zyzanski et al. (1990) found that high volume providers suffered from "lower rates of preventive services delivery, lower patient satisfaction, and a less positive doctor–patient relationship."

Studies of the State Children's Health Insurance Program (SCHIP) in Colorado, New York, Kansas, and Florida provide some insight into how the patient experience might change among people newly acquiring insurance. All of the studies show that uninsured children acquiring insurance under SCHIP had an increase in the likelihood of having a usual source of care, and most found that access for acute care improved as well (Duderstadt et al., 2006; Kempe et al., 2005; Holl et al., 2000; Szilagyi et al., 2000).

Several studies of SCHIP expansions have also shown that access to care and satisfaction with care improved most significantly for children who had been insured for more than one year and for those with special health care needs (Dick et al., 2004; Davidoff, Kenney, Dubay, 2005). A study of the effect of the Oregon Health Plan, a Medicaid waiver program, on the satisfaction of adults found that enrollees were much more likely to rate their ability to see a doctor as "excellent" or "good" than were low income privately insured and uninsured adults (Mitchell et al., 2002). They were also more likely to rate their health plan coverage as being "excellent" or "very good" and were more likely to be very satisfied with their overall quality of care.

References

Davidoff A, Kenney G, Dubay L, "Effects of the State Children's Health Insurance Program Expansions on Children with Chronic Health Conditions," Pediatrics, Vol. 116, No. 1, July 2005, pp. e34–e42.

Dick AW, Brach C, Allison RA, Shenkman E, Shone LP, Szilagyi PG, Klein JD, Lewit EM, "SCHIP's Impact in Three States: How Do the Most Vulnerable Children Fare?" Health Affairs, Vol. 23, No. 5, September/October 2004, pp. 63–75.

Duderstadt KG, Hughes DC, Soobader M-J, Newacheck PW, "The Impact of Public Insurance Expansions on Children's Access and Use of Care," Pediatrics, Vol. 118, No. 4, October 2006, pp. 1676–1682.

Holl JL, Szilagyi PG, Rodewald LE, Shone LP, Zwanziger J, Mukamel DB, Trafton S, Dick AW, Barth R, Raubertas RF, "Evaluation of New York State's Child Health Plus: Access, Utilization, Quality of Care, and Health Status," Pediatrics, Vol. 105, No. 3, Suppl., March 2000, pp. 711–718.

Kempe A, Beaty BL, Crane LA, Stokstad J, Barrow J, Belman S, Steiner JF, "Changes in Access, Utilization, and Quality of Care After Enrollment into a State Child Health Insurance Plan," Pediatrics, Vol. 115, No. 2, February 1, 2005, pp. 364–371.

Mitchell JB, Haber SG, Khatutsky G, Donoghue S, "Impact of the Oregon Health Plan on Access and Satisfaction of Adults with Low Income," Health Services Research, Vol. 37, No. 1, February 2002, pp. 19–39.

Newacheck PW, Hughes DC, Stoddard JJ, "Children's Access to Primary Care: Difference by Race, Income and Insurance Status," Pediatrics, Vol. 97, No. 1, January 1996, pp. 26–32.

Schoen C, DesRoches C, "Uninsured and Unstably Insured: The Importance of Continuous Coverage," Health Services Research, Vol. 35, No. 1, Pt. 2, April 2000, pp. 187–206.

Schoen C, Lyons B, Rowland D, Davis K, Puleo E, "Insurance Matters for Low-Income Adults: Results from a Five-State Survey," Health Affairs, Vol. 16, No. 5, September/October 1997, pp. 163–171.

Szilagyi PG, Zwanziger J, Rodewald LE, Holl JL, Mukamel DB, Trafton S, Shone LP, Dick AW, Jarrell L, Raubertas RF, "Evaluation of a State Health Insurance Program for Low-Income Children: Implications for State Child Health Insurance Programs," Pediatrics, Vol. 105, No. 2, February 2000, pp. 363–371.

Zyzanski SJ, Stange KC, Langa D, Flock SA, "Trade-Offs in High Volume Primary Care Practice," Journal of Family Practice, Vol. 46, No. 5, May 1998, pp. 397–402.

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Health

An individual mandate will result in an increase in life expectancy among those who are newly insured and will result in an additional 1 to 4 million life years:

  • We estimate from our model an increase of about 1 to 4 million life years, depending on the design of the individual mandate. Read more below
  • Theory and published studies suggest that, if an individual mandate increases rates of coverage, the health of some groups should improve. Read more below
  • The magnitude of the effect on health may depend on the health of an individual before gaining insurance and on other socioeconomic factors, as well as on changes in access afforded by health insurance and the reliability of care received. Read more below

We estimate an increase of about 1 to 4 million life years, depending on the design of the individual mandate.

In our analysis, we used projections from the RAND Future Elderly Model to determine the increase in life expectancy attributable to a change in insurance status (Goldman et al., 2004). We used the following assumptions:

  • We assumed that individuals who obtain coverage under this policy option would retain that coverage continuously until they become eligible for Medicare at age 65.
  • We further assumed that those who obtain insurance change from the expected mortality rate for those who are uninsured to the expected mortality rate for those who are insured.
  • We assumed no difference in mortality among the population over age 65 based on their insurance status prior to becoming eligible for Medicare; therefore, we assumed that all of the effect of becoming newly insured occurs between becoming insured and age 65, a mortality difference of 9.06 percent.1
  • We assumed that there are no changes in rates of treatment or in the effectiveness with which medical care is delivered as a result of this policy change.

In the table, we show the expected total gains in life years that we estimate would result from the implementation of an individual mandate. The estimate is a function of the number of additional people with coverage, so the scenarios that produce the largest increases in coverage will also produce the largest increases in life years. We estimate that the country could gain about 1—4 million life years as a result of this policy change.

Expected Total Gains in Life Years with an Individual Mandate,
by Level of Subsidy and Size of Penalty for Noncompliance
  Penalty (as a Percentage of the Premium in the Purchasing Pool) Level of Subsidy
No Subsidy Full Subsidy for Persons
Below 100% FPL Plus:
Partial Subsidy for Households up to 200% FPL Partial Subsidy for Households up to 300% FPL Partial Subsidy for Households up to 400% FPL
Life years gained (millions) 0   0.9 1.0 1.1
30 1.2 1.7 1.9 2.2
50 1.6 2.2 2.6 2.8
80 2.4 2.9 3.5 3.8

SOURCE: RAND COMPARE microsimulation modeling results, December 28, 2008.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level (FPL); a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the table (200 percent, 300 percent and 400 percent respectively). Penalties are calculated as the percent of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

The results presented here are based on microsimulation analyses, which use what is known from the literature about the relationship between health (in this case, mortality rates) and insurance to estimate the effect of a policy change. However, published studies to date are generally cross-sectional comparisons, and we have no experimental or longitudinal data to use for estimating how a change in insurance status affects a population of individuals.

Theory and published studies suggest that, if an individual mandate increases rates of coverage, the health of some groups should improve.

Consistent with our modeling results, the literature suggests that there may be a modest relationship between insurance status and health outcomes, but methodological issues have made it difficult to make an accurate quantitative estimate. The RAND Health Insurance Experiment (Newhouse and Insurance Experiment Group, 1993) randomly assigned families to health plans, providing an opportunity to assess how benefit generosity affected health outcomes in a setting in which health was unrelated to insurance choice. Overall, the study found that benefit generosity had a negligible influence on health outcomes for the general population, although there were some benefits for low income participants who were in poor health at the beginning of the study. However, since everyone in the RAND Health Insurance Experiment had at least minimal health coverage, the study cannot necessarily be used to gauge the effect of becoming insured.

Levy and Meltzer (2008) reviewed the literature on the relationship between health insurance and health and drew several conclusions. First, most studies were not able to establish a causal relationship between health insurance and health because they did not account for the multiple other factors that affect these two variables or the fact that health itself affects insurance status. Second, there is substantial evidence that health insurance improves health among vulnerable populations, such as infants, children, individuals with HIV, and some low income adults, but there is less evidence of this relationship for other groups. Finally, Levy and Meltzer suggest that it may be difficult to generalize the results of the studies thus far.

Hadley (2003) reviewed the literature from the past 25 years on this subject and found that the general consensus among studies was that providing those who are uninsured with health insurance would result in improved health. Hadley's "best guess" is that mortality would decrease between 4–25 percent for previously uninsured people. Our modeled estimate of 9.06 percent is contained within this range. Although Hadley cites methodological difficulties within these studies, he acknowledges the consistency in the findings of improved health with insurance in many populations and disease states.

The magnitude of the effect on health may depend on the health of an individual before gaining insurance and on other socioeconomic factors, as well as on changes in access afforded by health insurance and the reliability of care received.

Medical care represents only one of the many determinants of health (others being behavior, socioeconomic status, education, and genetics, for example); consequently, improved access to medical care via insurance changes may have only a modest effect on health. McGinnis, Williams–Russo, and Knickman (2002) suggest that only 10–15 percent of preventable deaths are attributable to problems with medical care. In addition, there are other important determinants of access apart from health insurance coverage.

Estimating how insurance affects health poses significant methodological challenges. In particular, insurance status and health are not independent; that is, health can directly affect the ability or desire to obtain coverage. Healthy people may be less likely to purchase insurance because they anticipate having minimal health expenditures. Sick individuals may be unable to purchase individual policies. Other unobservable characteristics may also influence both health and insurance status. Thus, when researchers attempt to study differences in health based on health insurance status, it can be difficult to discern whether having insurance really causes improved health.

  1. We had previously generated mortality tables for insured and uninsured persons age 50 to 64 and found a mortality difference of 9.06. Because the mortality rates up to age 50 are comparatively small, we assume that this mortality difference applies to everyone under age 65. This assumption has very little effect on the overall expected life years gained by an insured individual compared with an uninsured individual.

References

Goldman DP, Shekelle PG, Battacharya J, Hurd MD, Joyce GF, Lakdawalla DN, Matsui DH, Newberry SJ, Panis CWA, Shang B, Health Status and Medical Treatment of the Future Elderly: Final Report, Santa Monica, Calif.: RAND Corporation, TR-169-CMS, August 2004. As of March 17, 2009:http://www.rand.org/pubs/technical_reports/TR169/

Hadley J, "Sicker and Poorer—The Consequences of Being Uninsured: A Review of the Research on the Relationship Between Health Insurance, Medical Care Use, Health, Work, and Income," Medical Care Research and Review, Vol. 60, No. 2, Suppl., June 1, 2003, pp. 3S&ndash75S.

Levy H, Meltzer D, "The Impact of Health Insurance on Health," Annual Review of Public Health, Vol. 29, April 2008, pp. 399–409.

McGinnis JM, Williams-Russo P, Knickman JR, "The Case for More Active Policy Attention to Health Promotion," Health Affairs, Vol. 21, No. 2, March/April 2002, pp. 78–93.

Newhouse JP, Insurance Experiment Group, Free for All? Lessons from the RAND Health Insurance Experiment, Cambridge, Mass.: Harvard University Press, 1993.

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Coverage

An individual mandate will increase the number of people with coverage by 9 to 34 million, depending on the design of the policy:

  • From our modeling results, we estimate that an individual mandate combined with a subsidy and a 50 percent noncompliance penalty will result in about 15 to 26 million more people having insurance, depending on the generosity of the subsidy. Read more below
  • Using our model, we find that both the subsidy schedule and the size of the penalty are important in determining the likely effect of an individual mandate on coverage. Read more below
  • From our model, we find that the administrative cost in the purchasing pool has little effect on coverage. Read more below
  • The Massachusetts reform although not identical to the scenarios we modeled, resulted in a larger reduction in "uninsurance" than our model would predict. Read more below
  • Our model predicts considerably smaller effects of an individual mandate than do those of other researchers, although the design of the scenarios we modeled is not directly comparable to the scenarios modeled by others. Read more below

From our modeling results, we estimate that an individual mandate combined with a subsidy and a 50 percent noncompliance penalty will result in about 15 to 26 million more people having insurance, depending on the generosity of the subsidy.

An individual mandate is likely to reduce the number of those who are uninsured by 33—57 percent, depending on the design of the policy. We modeled an individual mandate with the following design:

  • All individuals, without exception, are required to obtain health insurance coverage.
  • A national purchasing pool is created, with private insurers offering coverage that complies with federal rating regulations.
  • Plans participating in the national purchasing pool agree to sell policies to all who apply (i.e., guaranteed issue required for participation).
  • Premiums in the purchasing pool can vary only with age, and four rate bands are created (0–17, 18–29, 30–49, 50–64 years).
  • We modeled an average plan for the purchasing pool using an actuarial value of 0.70, which corresponds to a plan that is slightly more generous than a typical non-group plan and less generous than a typical employer plan.
  • We estimated premiums for plans in the purchasing pool in our model using the experience of enrollees; they range from approximately $1,000—$1,400 (for enrollees under age 17) to $4,400—$6,200 (for enrollees age 50 to 64).1
  • The administrative cost of the purchasing pool in the results presented is 15 percent, but we also tested administrative costs of 25 percent.
  • Subsidies for the purchase of insurance are available only for policies from the purchasing pool. We tested a variety of subsidy structures related to income. Full subsidies are available for people whose income falls below a minimum threshold (we use 100 percent of FPL in the presented results, but we also tested 150 percent of FPL). Partial subsidies are available on a linear decreasing sliding scale up to a maximum income threshold (we tested 150 percent, 200 percent, 250 percent, 300 percent, and 400 percent of the FPL).
  • Individuals who are Medicaid eligible and individuals with access to employer sponsored insurance (either their own employer or that of a spouse) cannot buy into the purchasing pool. This follows the structure of the Massachusetts plan.
  • Penalties for failure to purchase insurance are calculated as a percentage of the premium an individual would have paid in the purchasing pool. We tested four penalty levels: 0 percent, 30 percent, 50 percent, and 80 percent.

We made the following assumptions in modeling an individual mandate:

  • The penalty is perfectly enforced. This assumption can be relaxed in future iterations of the model.
  • Households in which all members are fully insured through employer sponsored insurance make no change in response to the mandate. (By households, we technically mean health insurance eligibility units—that is, the group of people that, together, are eligible for private and/or public coverage.
  • Households with at least one uninsured member make decisions about how to respond to the individual mandate, the penalty for noncompliance, and the availability of subsidies, if applicable, within a utility maximization framework. This framework is described in greater detail in the modeling overview paper.
  • We include in the model a parameter that accounts for the apparent disutility of insurance held by those who are currently eligible for Medicaid or employer sponsored insurance and elect not to participate.

In the table, we show the effect of an individual mandate on the number of people who are newly insured. Using our model, we estimate that an individual mandate combined with a subsidy available to people who purchase insurance in a national purchasing pool and a 50 percent noncompliance penalty will result in a 46–57 percent decrease in the number of people without insurance. In the absence of a subsidy, and assuming a 50 percent noncompliance penalty, the decrease in the number of insured would be 14.6 million (33 percent).

Number of Newly Insured People Resulting from an Individual Mandate,
at Different Subsidy Levels and Noncompliance Penalty Sizes
  Penalty (as a Percentage of the Premium in the Purchasing Pool) Level of Subsidy
No Subsidy Full Subsidy for Persons
Below 100% of FPL Plus:
Partial Subsidy For Households up to 200% FPL Partial Subsidy For Households up to 300% FPL Partial Subsidy For Households up to 400% FPL
Number of Newly Insured People(in millions) 30 11.2 16.2 18.6 20.8
50 14.6 20.8 23.9 25.9
80 21.5 27.1 31.5 34.1

SOURCE: RAND COMPARE microsimulation modeling results, December 28, 2008, and based on 2007 dollars and spending levels.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the table (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

Using our model, we find that both the subsidy schedule and the size of the penalty are important in determining the likely effect of an individual mandate on coverage.

Figure 1 shows the effect of penalties on the number of net newly insured people at different subsidy levels. In a scenario with no penalty and no subsidy, we do not expect to see any change in the number of people choosing to become insured. A subsidy in the absence of a penalty results in 8.9 to 10.5 million additional persons becoming insured. Penalties in the absence of subsidies increase the number of persons with insurance by 11.2 to 21.5 million. Our model suggests, overall, that a high penalty is more effective than a high subsidy for increasing the number of people with insurance. As the penalties increase, the number of people becoming newly insured increases for all subsidy levels. However, preliminary analysis (not shown here) suggests that, at low levels of income, a penalty and a subsidy are good substitutes for each other.

 Effect of Penalty Size on Number of Newly Insured by Subsidy Level SOURCE: RAND COMPARE microsimulation modeling results, December 28, 2008.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the legend (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

From our model, we find that the administrative cost in the purchasing pool has little effect on coverage.

In the results shown here, we have assumed an administrative cost of 15 percent, which is much lower than the average administrative cost in the non–group market. For the purpose of a sensitivity analysis, we have also modeled an administrative cost of 25 percent. In terms of coverage, there is little difference between these two scenarios. In fact, a higher administrative cost generates higher premiums and makes insurance less attractive; however, since the penalty for noncompliance is proportional to premiums, higher premiums also make being uninsured less attractive, and these two effects tend to cancel each other.

The Massachusetts reform while not identical to the scenarios we modeled, resulted in a larger reduction in "uninsurance" than our model would predict.

Massachusetts is the only state to have implemented an individual mandate to purchase health insurance. The mandate was part of a health reform package that contained a number of other elements including an employer mandate, insurance market reforms, and the establishment of a new option for purchasing insurance (Long, 2008). The penalty in the first year for those who did not comply with the mandate and were judged to be financially able to purchase insurance was the loss of the personal tax exemption ($219 for an individual). The design of the Massachusetts mandate in the first year is similar to our option with no penalty and a moderate subsidy. In the first year, Massachusetts had about a 46 percent decline in the proportion of those who were uninsured (from 13 percent to 7 percent) (McDonough et al., 2008). Among the 355,000 newly insured, about half took advantage of the subsidies, 15 percent enrolled in Medicaid, and 35 percent obtained private coverage. Our model predicts lower overall effects on coverage (about a 20 percent increase); the experience in Massachusetts is more consistent with the 30 percent penalty and high subsidy scenario we modeled, or a 50 percent penalty and a moderate subsidy scenario. Some of the difference may be explained by the fact that our scenario does not include expanded eligibility for Medicaid. We also did not include in our scenario the full extent of market reforms introduced in Massachusetts, many of which have had the effect of reducing the cost of non–group premiums (McDonough et al, 2008). Although the Massachusetts reform included an employer mandate, it is not clear that this mandate has played much of a role to date in the effect of the reform (Gabel, Whitmore, Pickreign, 2008).

Our model predicts considerably smaller effects of an individual mandate than do those of other researchers, although the design of the scenarios we modeled is not directly comparable to the scenarios modeled by others.

Lambrew and Gruber (2006/2007) modeled an individual mandate combined with a Medicaid expansion, tax credit, and a purchasing pool. They predicted that an individual mandate combined with either less or more generous subsidies would result in all previously uninsured persons becoming insured. Most of the effect they observed came from people taking insurance in the new group (subsidized) pool (63–67 percent of the newly insured people). We also find that subsidies combined with a purchasing pool play an important role in reducing the number of those who are uninsured.

Gruber (2008) modeled the effect of an individual mandate combined with subsidies and a voucher targeted at people with an offer of employer sponsored coverage who have not obtained insurance. He modeled a subsidy whose generosity decreases with income and extends up to 400 percent of FPL. He assumed that the penalty is strong enough to guarantee a decrease of 97 percent in the number of those who are uninsured. In our model, to achieve such a high level of coverage, we need penalties close to 100 percent and subsidies that extend to 400 percent of FPL. However, our results are not directly comparable, since we cannot disentangle the effects of vouchers for employer sponsored coverage in the policy option modeled by Gruber.

  1. Because premiums are determined endogenously, the specific value varies in each modeled scenario. For example, realized premiums are higher in scenarios in which the penalty for noncompliance with the mandate is low, since some healthy people will opt to pay the penalty rather than obtaining insurance in the purchasing pool.

References

Gabel JR, Whitmore H, Pickreign J, "Report from Massachusetts: Employers Largely Support Health Care Reform, and Few Signs of Crowd-Out Appear," Health Affairs, Web Exclusives [Epub November 14, 2007], Vol. 27, No. 1, January/February 2008, pp. w13–w23.

Gruber J, "Covering the Uninsured in the U.S.," Cambridge, Mass.: National Bureau of Economic Research, Working Paper w13758, January 2008. As of January 6, 2009: http://www.nber.org/papers/w13758

Lambrew JM, Gruber J, "Money and Mandates: Relative Effects of Key Policy Levers in Expanding Health Insurance Coverage to All Americans," Inquiry, Vol. 43, No. 4, Winter 2006/2007, pp. 333–344.

Long SK, "On the Road to Universal Coverage: Impacts of Reform in Massachusetts at One Year," Health Affairs, Web Exclusives, [Epub June 3 2008], Vol. 27, No. 4, July/August 2008, pp. w270–w284.

McDonough JE, Rosman B, Butt M, Tucker L, Howe LK, "Massachusetts Health Reform Implementation: Major Progress and Future Challenges," Health Affairs, Web Exclusives [Epub June 3 2008], Vol. 27, No. 4, July/August 2008, pp. w285–w297

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Capacity

An individual mandate is not expected to change the overall capacity of the health care system:

  • We would not expect this policy option to change the capacity of the overall health system. Read more below
  • No empirical studies directly evaluate the effect of coverage changes on capacity. Read more below

We would not expect this policy option to change the capacity of the overall health system.

Capacity refers to the human resources (personnel and their productivity) and capital (medical equipment, hospitals, etc.) of the health care system. There is no direct connection between capacity of the health system and coverage. Furthermore, based on the design of the policy option we modeled, 3–11 percent of the population would become newly insured, resulting in little change in the demand for services or in the amount of money available to pay for those services (see the discussion under Coverage).

We do not expect that the changes in utilization among those who are newly insured will create a major change in market conditions. The size of any resulting change in utilization depends on assumptions about the extent to which demand for services will increase because of coverage, as well as the extent to which utilization patterns will change if formerly uninsured people substitute preventive, primary, and chronic care for acute care. We also assumed that the supply of health care resources would not adjust quickly to changes in demand.

We do not know whether any geographic areas will experience a significant change in market conditions based on a significant local increase in the proportion of people with insurance coverage whose utilization increases significantly. Areas with high rates of uninsurance today may see much steeper increases in the proportion of the population with insurance, which might lead to larger increases in demand for services. This increased demand could lead to a perceived reduction in the effective capacity of an area, because changes in the supply of health professionals, hospital beds, or many other facilities cannot occur quickly. We also do not know whether other trends in capacity related to economic conditions (e.g., changes in availability of hospital supply, primary care physicians, retail clinics) will affect the ability of newly insured individuals to access care.

No empirical studies directly evaluate the effect of coverage changes on capacity.

Empirical evidence suggests that increased coverage leads to increased health care utilization, which could, in turn, affect capacity. Many studies have shown that uninsured people use fewer health care services than insured people, and that changes in coverage are associated with changes in health care utilization. Buchmueller et al.'s (2005) review suggests that utilization for all types of services increases when coverage is expanded. Buchmueller et al. note that the effect of utilization may be overstated, since utilization will vary across subgroups. For example, if coverage is expanded primarily to young, healthy individuals, we would not expect a large increase in utilization. There is also evidence that expanding insurance increases the total number of hospitalizations for children but reduces avoidable hospitalizations (Dafny and Gruber, 2000).

References

Buchmueller TC, Grumbach K, Kronick R, Kahn JG, "The Effect of Health Insurance on Medical Care Utilization and Implications for Insurance Expansion: A Review of the Literature," Medical Care Research and Review, Vol. 62, No. 1, February 1, 2005, pp. 3–30.

Dafny L, Gruber J, "Does Public Insurance Improve the Efficiency of Medical Care? Medicaid Expansions and Child Hospitalizations," Cambridge, Mass.: National Bureau of Economic Research, Working Paper 7555, February 2000. As of January 10, 2009: http://www.nber.org/papers/w7555.

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Operational Feasibility

Implementing an individual mandate would be difficult, because of challenges in determining compliance with the mandate and enforcing penalties for noncompliance.

  • Implementation of an individual health insurance mandate presents challenges beyond those posed by other government mandates as a result of the scope of the population covered, the economic characteristics of health insurance, and the extent to which noncompliance with the mandate presents a public policy problem. Read more below
  • Both substantive features of a mandate, such as the amount of subsidies provided, and administrative elements, such as enforcement mechanisms, are relevant to compliance with and the effectiveness of the mandate. Both should be taken into account in the mandate's implementation. Read more below
  • Implementation of an individual mandate could make use of existing administrative mechanisms of the federal tax system, but significant expansion and improvements of these mechanisms would be necessary. Read more below

Implementation of an individual health insurance mandate presents challenges beyond those posed by other government mandates as a result of the scope of the population covered, the economic characteristics of health insurance, and the extent to which noncompliance with the mandate presents a public policy problem.

The operational feasibility of an individual mandate depends in part on the challenges that must be addressed to implement the mandate. An individual mandate would by definition provide an incentive for individuals to purchase health insurance when they might not have done so otherwise. However, no mandate will result in 100 percent compliance, as has been seen through the limited experience with health insurance mandates in some states and other countries, as well as through experience with mandates in other areas of the law, such as automobile insurance, child support, and taxation (Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008).

Scope. One challenge in implementing an individual health insurance mandate concerns the broad scope of the mandate. A universal health insurance mandate would apply to all citizens and legal residents of the United States. By comparison, automobile insurance mandates, for example, apply only to automobile owners, and tax laws exclude large portions of the population who earn insufficient income (Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008). Because of its scope, an individual mandate would require new administrative mechanisms or significant expansion and adaptation of existing ones.

Economic characteristics of health insurance. Another challenge in implementing an individual mandate concerns the economic characteristics of health insurance, which can create a mismatch between the government's desire for compliance and individuals' interests in minimizing their own costs and getting sufficient value for their money. Health insurance can be very expensive, and there is no connection between the cost of the insurance and an individual's household income. As a result, the economic effects of an individual mandate might be like those of a regressive tax (a tax that is applied uniformly--the less individuals earn, the higher their tax rate). In addition, the cost of health insurance may serve as a disincentive for some healthy individuals to purchase insurance, especially when the cost of the insurance reflects a degree of "community rating" (i.e., cost calculated in relation to the health status of the entire community) and thus may exceed the value of the insurance to healthy individuals (Glied, 2008b; Steuerle, 1994).

Potential for noncompliance. Noncompliance for an individual mandate may become an especially serious concern because of the frequency with which families and individuals rely on health insurance to pay for health care needs. Widespread failure to purchase health insurance under an individual mandate increases health insurance costs for other members of society and presents practical administrative and financing problems when noncomplying individuals need health care. In contrast, widespread failure to purchase automobile liability insurance, while presenting problems for the overall security of motorists, will have its principal effects on the relatively infrequent cases when automobile accidents occur.

Evaluation of these challenges in implementing an individual mandate should, however, also take into account the fact that health insurance mandates appear to have been implemented successfully in both the Netherlands (Enthoven and van de Ven, 2007; Knotterus and ten Velden, 2007) and Switzerland (Herzlinger and Parsa-Parsi, 2004), although the application of these mandates to the United States might be limited because of differences in political structures and cultures. The recent experience in Massachusetts provides another example of the successful implementation of an individual health insurance mandate, which appears to have been carried out without inordinate problems in administration. Other challenges have arisen, however, such as in the area of cost control (Steinbrook, 2008 and 2009).

Both substantive features of a mandate, such as the amount of subsidies provided, and administrative elements, such as enforcement mechanisms, are relevant to compliance with and the effectiveness of the mandate. Both should be taken into account in the mandate's implementation.

Previous experience with health insurance and other mandates indicates that compliance with an individual health insurance mandate will depend primarily on six factors, which include both substantive and administrative features of the program. These factors are (1) the value of insurance to the individual; (2) the cost of the insurance; (3) the cost of compliance to the individual, including administrative burden and other transaction costs; (4) the amount of any subsidy provided to help individuals purchase insurance; (5) the penalty for noncompliance; and (6) the likelihood of detection and application of the penalty (Glied, 2008b; Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008; Steuerle, 1994). Of these six factors, items 3, 5, and 6 can be considered more administrative in nature, while 1, 2, and 4 can be considered substantive.

It has been suggested that the symbolic effect of a mandate may also lead to compliance beyond the effect of these major factors (Glied, 2008b), but the experience with mandates noted above indicates that these factors dominate. RAND's modeling is consistent with this assessment, in that it shows that a mandate would not increase health insurance coverage without a penalty or a subsidy (see the figure).

 Effect of Penalty Size on Number of Newly Insured People, by Subsidy Level SOURCE: RAND COMPARE microsimulation modeling results, December 28, 2008.
NOTES: Subsidies are constructed as follows: A full premium subsidy is available for households with incomes up to 100 percent of the federal poverty level; a partial premium subsidy is available on a sliding scale for households with incomes up to the maximum levels specified in the legend (200 percent, 300 percent, and 400 percent, respectively). Penalties are calculated as the percentage of the premium an individual would otherwise have had to pay to obtain insurance in the purchasing pool.

All six factors listed above affect an individual's overall assessment of whether to comply with the mandate. This assessment from the standpoint of the individual can be summarized as follows:

  • If the net value of the insurance to the individual is greater than the cost of noncompliance, the individual would be expected to comply with the mandate. For this purpose, the net value of the insurance is its value less its net cost.
  • The net cost of the insurance is in turn the cost of purchasing the insurance plus the cost of compliance, less the amount of the subsidy.
  • The cost of noncompliance is determined by the amount of the penalty and the likelihood of detection and imposition of the penalty.

Thus, the effectiveness of an individual mandate would most likely benefit from coordinating the design of substantive and administrative components. Changes in substantive elements of the policy, such as the value of mandated insurance and the level of subsidies, affect compliance and ease of administration by altering the balance between the net economic benefits and the burdens of the mandate. Leaving enforcement aside, the more generous the subsidy is relative to the cost of insurance, the greater is compliance.

Disadvantages of more generous subsidies, however, would be a corresponding increase in government spending on the program and concern about potential "crowd out" of private insurance that would otherwise be provided (Glied, 2008b). A well-functioning system of enforcement can be used to increase compliance through the detection of noncompliance and enforcement of penalties. This detection may in turn reduce the level of subsidies needed and the likelihood of a possible crowd-out effect.

Similarly, the effectiveness of subsidies as an incentive for compliance depends on how effectively they are administered--i.e., how easily and accurately they are made available to qualifying individuals. Simplified procedures for purchasing insurance, obtaining insurance, and demonstrating compliance can reduce costs for individuals and thereby enhance compliance. One complicating factor is that, because of marked geographic variations in health care costs, a subsidy amount that does not vary across geographic locations will have significantly different economic values in different regions (Newhouse and Reischauer, 2004; Tanner, 2006). Adjusting the subsidy to address this problem, however, would likely also increase the complexity of administration and enforcement.

Regardless of how generous subsidy amounts are relatively to the cost of insurance and how well they are administered, there will be some remaining noncompliance issues that will necessitate direct enforcement. The effectiveness of enforcement depends on the level of penalties imposed and on how well the administrative mechanisms used to detect noncompliance and to enforce penalties function. As noted above, compliance rises with increased penalties: If penalties are too low, individuals may choose to avoid the mandate rather than comply. On the other hand, penalties that are too high may be viewed as unlikely to be enforced, thus reducing their effectiveness (Glied, Hartz, and Giorgi, 2007). Moreover, the effectiveness of penalties varies with the likelihood that noncompliance will be detected and that penalties will be imposed. Existing administrative mechanisms (such as the tax system) could be used to minimize the additional cost and intrusiveness of mechanisms for enforcing a new mandate. These issues are discussed further below.

Implementation of an individual mandate could make use of existing administrative mechanisms of the federal tax system, but significant expansion and improvements of these mechanisms would be necessary.

The extent to which an individual mandate would require new administrative mechanisms and processes is another aspect of its feasibility. Implementation of the individual mandate would require mechanisms to deliver any subsidies and to detect and administer penalties for noncompliance. An effective enforcement mechanism would be necessary to make penalties credible, since the effectiveness of penalties depends on both their magnitude and likelihood of application (Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008).

The tax system provides one option for administering an individual mandate. The plan enacted in Massachusetts in 2006 uses the state tax system for this purpose (Holtzblatt, 2008). One reason for this choice is the similarity between enforcing the mandate and enforcing the tax laws. A national individual mandate would, like federal income tax laws, apply to all households and individuals. Compliance with the income tax laws is the responsibility of the Internal Revenue Service (IRS), and no other existing administrative agency has comparably immediate and frequent contact with the entire population (Glied, 2008a).

On the other hand, an individual mandate for health insurance would need to extend to individuals who are exempt from federal income tax filing requirements. The Joint Committee on Taxation estimated that approximately 28 million potential "tax units" were under no obligation to file U.S. tax returns in 2008 (Holtzblatt, 2008). Thus, implementation of an individual mandate through the federal tax system, including monitoring of compliance and enforcement, would also need to develop a means of including this subset of the population, thus imposing new costs on both the IRS and the private individuals involved. It has been noted that the IRS may find it difficult to reach individuals with whom it now has little or no contact (Holtzblatt, 2008; Steuerle, 1994).

Experience with the tax laws indicates that compliance increases considerably when third parties report individuals' income (e.g., wages by employers and interest income by financial institutions) to the IRS, rather than relying solely on reports of income in individual tax returns. For income that is subject to third party information reporting and automatic matching programs, the rate of misreporting of income is 4.5 percent, while for income without third party verification (such as for rents and royalties), the rate of misreporting is nearly 54 percent (Holtzblatt, 2008).

Accurate administration of subsidies and monitoring of compliance with an individual mandate would require verification of income and the purchase of insurance, and then matching the two items (Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008; Steuerle, 1994). Income information is already provided to the IRS by employers for the working population, but verification for nonworkers would likely involve other government agencies and, in some cases, may be difficult and costly. Verification of the purchase of insurance would necessitate the participation of insurers. Finally, matching of information from all of these sources at the federal level would require enhancement of current data systems, possibly at considerable expense (Glied, Hartz, and Giorgi, 2007; Holtzblatt, 2008).

A related concern is whether an individual mandate should use an annual accounting period, such as that used by the federal tax system. It has been argued that such an arrangement may not provide adequate monitoring of compliance with an individual mandate, which is designed to ensure that individuals have health insurance at all times, not only at a particular time of the year (Holtzblatt, 2008). However, it has also been argued that enforcement of and compliance with an individual mandate would be simplified, and thus enhanced, if those subject to the mandate purchase coverage at a specified time (e.g., a single annual open enrollment period), while enforcement occurs in conjunction with that arrangement (Glied, Hartz, and Giorgi, 2007).

Effective administration of subsidies also requires a mechanism to assist low income households to purchase insurance in a timely manner. Many low income households will find it difficult or impossible to afford the purchase of meaningful health insurance without the subsidy, but making the subsidy useful for the purchase of insurance would require payment of the subsidy at the time of purchase, which may be outside the scope of the annual tax accounting period. Such so-called "advance payment" is allowed for two existing refundable income tax credits: the earned income credit and the health coverage tax credit. The former, however, does not involve verification of both income and insurance purchase, which, as discussed above, is needed to administer an individual health insurance mandate. While the health coverage tax credit involves such mechanisms and is designed specifically for the purchase of insurance, it is a relatively small scale program providing benefits to a monthly average of only about 16,000 households (Dorn, 2008). Moreover, the advance payment mechanisms for both tax credits have a low level of intake (Dorn, 2008; Holtzblatt, 2008). Addressing these challenges for the individual mandate would likely require improved outreach and a significant expansion of administrative mechanisms, both of which would add to the cost of implementing the mandate.

All of these considerations regarding implementation of the individual mandate are important, but assessment of the feasibility of implementing an individual mandate should also take into account the successful introduction of mandates in both the Netherlands and Switzerland. In addition, the State of Massachusetts appears to have been relatively successful thus far in implementing a mandate, albeit at the state level and with some components of its system still in the process of development (Holtzblatt, 2008; Steinbrook, 2008).

References

Dorn S, "Comment," in Aaron HJ, Burman LE, Using Taxes to Reform Health Insurance: Pitfalls and Promises, Washington, D.C.: The Brookings Institution, 2008, pp. 199–207.

Enthoven AC, van de Ven WPMM, "Going Dutch—Managed-Competition Health Insurance in the Netherlands," New England Journal of Medicine, Vol. 357, No. 24, December 13, 2007, pp. 2421–2423.

Glied SA, "Comment," in Aaron HJ, Burman LE, Using Taxes to Reform Health Insurance: Pitfalls and Promises, Washington, D.C.: The Brookings Institution, 2008a, pp. 208–210.

Glied SA, "Universal Coverage One Head at Time--The Risks and Benefits of Individual Health Insurance Mandates," New England Journal of Medicine, Vol. 358, No. 15, April 10, 2008b, pp. 1540–1542.

Glied SA, Hartz J, Giorgi G, "Consider It Done? The Likely Efficacy of Mandates for Health Insurance," Health Affairs, Vol. 26, No. 6, November/December 2007, pp. 1612—1621

Herzlinger RE, Parsa-Parsi R, "Consumer-Driven Health Care: Lessons from Switzerland," Journal of the American Medical Association, Vol. 292, No. 10, September 8, 2004, pp. 1213–1220.

Holtzblatt J, "The Challenges of Implementing Health Reform Through the Tax System," in Aaron HJ, Burman LE, Using Taxes to Reform Health Insurance: Pitfalls and Promises, Washington, D.C.: The Brookings Institution, 2008, pp. 171–198.

Knotterus JA, ten Velden GHM, "Dutch Doctors and Their Patients--Effects of Health Care Reform in the Netherlands," New England Journal of Medicine, Vol. 357, No. 24, December 13, 2007, pp. 2424–2426.

Newhouse JP, Reischauer RD, "The Institute of Medicine Committee's Clarion Call for Universal Coverage," Health Affairs, Web Exclusive, March 31, 2004, pp. W4-179—W4-183.

Steinbrook R, "Health Care Reform in Massachusetts—Expanding Coverage, Escalating Costs," New England Journal of Medicine, Vol. 358, No. 26, June 26, 2008, pp. 2757–2760.

Steinbrook R, "The End of Fee-for-Service Medicine? Proposals for Payment Reform in Massachusetts," New England Journal of Medicine, Vol. 361, No. 11, September 10, 2009, pp. 1036–1038.

Steuerle CE, "Implementing Employer and Individual Mandates," Health Affairs, Vol. 13, No. 2, Spring (II) 1994, pp. 54–68.

Tanner MD, Individual Mandates for Health Insurance: Slippery Slope to National Health Care, Washington, D.C.: Cato Institute, Policy Analysis No. 565, April 5, 2006.

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