research paper on cost benefit

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Cost-Benefit Analysis versus Cost-Effectiveness Analysis from a Societal Perspective in Healthcare

Cost-effectiveness analysis (CEA) is the main way that economic evaluations are carried out in the health care field. However, CEA has limited validity in deciding whether any health care evaluation is socially worthwhile and hence justifies funding. Cost-Benefit Analysis (CBA) is the economic evaluation method that should be used to help decide what to invest in when the objective is to record the impact on everyone in society. Cost-utility analysis (CUA), which has its roots in CEA, can be converted into CBA under certain circumstances that are not general. In this article, the strengths and weaknesses of CEA relative to CBA are analyzed in stages, starting in its most classical form and then proceeding via CUA to end up as CBA. The analysis takes place mainly in the context of five actual dementia interventions that have already been found to pass a CBA test. The CBA data is recast into CEA and CUA terms in tabular form in order that the contrast been CEA and CBA is most transparent. We find that how much of the fixed budget that is used up to fund other alternatives determines how much is left over to fund the particular intervention one is evaluating.

1. Introduction

Using cost-effectiveness analysis (CEA) in healthcare, by finding the intervention which produces a specific outcome at the lowest cost, may be useful when the perspective is limited to a hospital, healthcare provider, government agency or other healthcare institutional setting, especially when a budget has been provided to fund the least cost interventions. But, when the perspective is social, meaning everyone in society, whether they be family members, third parties or taxpayers is involved, and a budget has not been allocated, a CEA is completely insufficient for deciding priorities as to which interventions, if any, should be funded. In this context, cost-benefit analysis (CBA) is the more relevant economic evaluation method.

The purpose of this paper is to explain precisely why, and how, CBA is the more relevant evaluation method when one is taking a societal perspective in healthcare when evaluating interventions (For a CBA text that focuses solely on the field of heath care, see [ 1 ]). This will be carried out mainly in the context of evaluating five new interventions for reducing dementia symptoms. The new dementia interventions involved are: years of education (a dropout prevention program), Medicare eligibility (the extra services it provides), hearing aids (over a lifetime), vision correction (over a lifetime), and avoiding living in a nursing home (as residing in a nursing home increases dementia symptoms). What made these five interventions “new” was not that the medical literature was unaware of them; rather it was because they were recently fully evaluated using CBA, and newly shown to be worthwhile financing, see [ 2 ].

For the five dementia interventions we will be highlighting, estimation of benefits and costs were carried out using a large, national panel data set from the National Alzheimer’s Coordinating Center (NACC) (Alzheimer’s is the main category of dementia experienced throughout the US. In our NACC data used for making the economic evaluation calculations presented in this article, we cover all categories of dementia). However, in order that the contrast between using CEA and CBA be fully transparent, some of the data used for the CBA evaluations will be recast also into CEA terms.

The outline of the paper is as follows. We start the analysis by viewing CEA as a method of economic evaluation in its classical form, which involves covering mutually exclusive interventions with, and without, a budget constraint. Then we move on to the more general form of CEA which is called cost-utility analysis (CUA). CUA can be viewed as CEA, but it also can be converted into a CBA with one extension. This leads the analysis to CBA proper which, unlike CEA, can be applied to any type of healthcare intervention to assess whether it is socially worthwhile. In the discussion section, some of the background wider issues concerning CEA and dementia interventions are presented. We close with the summary and conclusions.

2. Cost-Effectiveness Analysis

2.1. cost-effectiveness analysis with a budget constraint.

Here we will present the findings of the evaluations of the five new dementia interventions using the CEA methodology. We will assume that the institutional setting involves the US government, say Medicare or Medicaid is making the expenditure decisions (Of the five dementia interventions, only education, Medicare eligibility, and avoiding nursing homes actually involved government involvement).

The starting point for a CEA is to define the effect unit that is to be costed for each of the interventions. For the five new dementia interventions, the effect was the reduction in dementia symptoms as measured by the Clinical Dementia Rating (CDR) scale, known as the CDR ® Dementia Staging Instrument , created by Washington University (The CDR is a measure of dementia severity used globally that is based primarily on a neurological exam and informant reporting, see [ 3 ]. A CDR was administered to each NACC participant at each visit by a clinician. There are six domains in the CDR: memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care. Each domain is assessed using a 0 to 3 interval (none, mild, moderate and severe) with a questionable response being scored as 0.5. The CDR-SB (the CDR sum of boxes) is the aggregate score across all six domains and this has a range of 0 to 18. It is important to understand that using dementia symptoms as the effect is basing cognitive impairment on a behavioral definition of dementia (interfering with activities of daily living) rather than the medical definition, which relies on brain pathology (for example fibers and plaques for Alzheimer’s). At this time, there are no interventions that can alter brain pathology, but there does already exist interventions that can reduce dementia symptoms.

To obtain the effect from any intervention, resources have to be allocated and they have to be costed. In CEA the costing is usually related just to the institution undertaking the financing, which is called the private costs. Although the costing for the CBA’s of the new dementia interventions was wider than this, we will assume, just for simplicity, that the CBA costs were the relevant ones for the CEA (We will relax this assumption when we deal with externalities when the analysis switches to social CBA).

Dividing these cost estimates by the outcome effects produces the cost-effectiveness ratios for each intervention. CEA proceeds by ranking each intervention from the lowest to the highest ratio. Table 1 shows how CEA would rank the five new dementia interventions. On the basis of this ranking of the five interventions, education would be the most cost-effective, and avoiding living in a nursing home would be judged the least-cost-effective.

Cost-Effectiveness of Various Dementia Interventions.

* The benefit and cost figures for nursing homes in [ 2 ] were in population terms. To convert them into per-person terms, one needs to divide by 1.1 million, the approximate number of older adults in Medicaid-financed nursing homes. The total costs of living in nursing homes in the CBA (which was the difference between total benefits of USD 1.93 trillion and the benefits without the nursing home cost savings, which was USD 1.87 trillion) was equal to USD 0.06 trillion. Dividing this sum by 1.1 million produces a cost per person of USD 54,545.

As to the issue of which interventions, if any, would actually be funded, the budget constraint needs to be specified, which involves knowing the amounts available to the government to devote to the interventions. We will consider three possible budget limits: $10,000, $20,000 or $70,000 per person. For a budget of $10,000, the government would only approve education, corrective lenses and Medicare eligibility, as the cumulative sum totals $8705. For a budget of $20,000, hearing aids would be added (making the total $17,2037). For a budget of $70,000 not even avoiding nursing homes, which has by far the largest effect, would be approved, as the total cost with this intervention added would amount to $71,748.

It is clear from this application of the CEA method to the new dementia interventions that the decision-maker who specified how large the budget for the institution was going to be, was effectively determining which interventions were worthwhile for that institution and therefore were going to be carried out. The fundamental weakness of CEA is that this budget decision would be made in advance of knowing what interventions were available, and what the costs and effects are likely to be of the interventions that were available.

2.2. Cost-Effectiveness Analysis without a Budget Constraint

In the absence of a pre-specified budget constraint, the main role of CEA as an intervention evaluation method is to indicate which dementia interventions can be eliminated from consideration and therefore not to be financed. There are two categories of interventions that can be eliminated.

The first category contains any interventions that are not effective. For example, take the case of placing an older adult for skilled nursing care in the custody of a nursing home. In the case of the interventions listed in Table 1 , if under consideration is living in a nursing home (that is, not avoiding residing in a nursing home), Table 1 informs the decision-maker that residing in a nursing home actually increases dementia symptoms by 3.3267 points. This intervention would be counterproductive and so should be eliminated from consideration. In the process, one does not experience the increased dementia symptoms and one obtains a cost saving of $54,545. It is because of this effect gain and the avoidance of incurring costs, that avoiding living in a nursing home becomes a productive intervention and can be included in Table 1 .

The second category of intervention that can be eliminated involves an effect gain, or a cost saving from an intervention not listed, but involves some variation of the ones listed. The listed ones are not mutually exclusive as both hearing aids and corrective lenses can both be approved (The listed ones must also be not repeatable, or else one can just fund one intervention over and over again if that is the one that is the most cost-effective). However, if a variation of a listed intervention is the new intervention being evaluated, then the new intervention is mutually exclusive, as both the listed and the new intervention cannot both take place at the same time. In this case, the cost-effectiveness of the particular listed intervention becomes the benchmark for deciding the fate of the new intervention. Thus, for example, consider instead of purchasing five sets of hearing aids over a lifetime, which is what the hearing aid intervention listed in Table 1 involved, one is now evaluating instead purchasing six hearing aids over one’s lifetime. If this sixth set costs more than $8498, yet does not produce greater effects, then it can safely be eliminated as it is dominated by the existing listed intervention (More generally, for mutually exclusive interventions where the new intervention has both a different cost and effect than an existing intervention, the CEA must be decided on the basis of the incremental cost-effectiveness ratios rather than average cost-effectiveness ratios that we have been using. See [ 4 ]).

As we have just seen, CEA without a budget constraint can be useful by eliminating some interventions that are not effective, and are dominated by variations of the listed interventions. However, the decision-maker still does not know with CEA whether any of the listed interventions in Table 1 are worthwhile funding.

2.3. Cost-Utility Analysis as Cost-Effectiveness Analysis

From the perspective of CEA, Cost-Utility Analysis (CUA) is a CEA that involves the most general health care outcome, which is a Quality Adjusted Life Year (QALY). A QALY is the product of the number of life-years affected (LY) and the quality of any one life year (QoL). In principle, every health care intervention that one can think of, that has an effect, must affect either the quantity or quality (or both) of a person’s life, and that is why it is the most general effect to use in a CEA.

Three of the new interventions listed in Table 1 had CBAs that used data that can be expressed in units of QALYs. In Table 2 we present the three QALY effects, and combine them with the costs from Table 1 , to form the cost-utility ratio which is the cost-effectiveness ratio in a CUA.

Cost-Utility Analysis of Various Dementia Interventions.

* The outcome measure in [ 2 ] for corrective lenses was in terms of mortality and the reduction was 0.0044. Multiplying this mortality reduction by a life expectancy of 23 years produces the equivalent QALY increase of 0.1012. ** The outcome measure in [ 2 ] for hearing aids was in terms of the quality of a life year and the reduction was 0.0295. Multiplying this quality of life increase by a life expectancy of 23 years produces the equivalent QALY increase of 0.6785.

What is interesting about the alternative cost-effectiveness ratios in Table 2 is that the ratios with QALYs as the measure of effect is much higher than when dementia symptoms were the measure of effect. This confirms the obvious point that CEA ratios very much depend on the specific effect measure it uses. Thus, CEA’s applicability is not general unless it is in the form of a CUA.

What is not so obvious is that an intervention’s chances of being approved is very much dependent on what other interventions are not being evaluated. Not appearing in Table 2 are the education and Medicare interventions, because QALY information was not available for these two interventions. Without consideration of these two interventions, Table 2 reveals that avoiding nursing homes would now be approved if the budget were USD 70,000 (as the total cost of the three interventions would be USD 63,808) while before it was rejected. Even with CEA in the form of a CUA, assigning a budget in advance of knowing which interventions will be evaluated, and actually funded, is not a rational economic evaluation method.

When no budget constraint has been assigned, to use CUA for decision-making purposes, CUA league tables are often referred to. These tables rank from lowest to highest, in terms of their cost per QALY, a host of interventions appearing in the literature. These tables are then used for comparison with the particular intervention one is evaluating using CUA, which in our case relates to dementia. Table 3 gives an abbreviated league table that appeared in [ 5 ] (The interventions in Mason et al.’s (1993) table are valued in 1990 UK pounds. To facilitate comparison with the US dementia interventions used in this article, which were mainly in 2000 dollars, the UK 1990 GBP value was raised to its 2000 GBP equivalent, using the consumer price index, and then converted to USD using the official foreign exchange rate. Thus, the conversion involved multiplying the GBP amount by 2.1818).

Cost-Utility Analysis of Various Non-Dementia Interventions.

Comparing the cost-utility ratios in Table 2 with those in Table 3 , we would conclude that none of the three new dementia interventions was as cost-effective as cholesterol testing and diet therapy, which had the lowest cost-utility ratio of all listed by [ 5 ]. However, all three dementia interventions were more cost-effective than Erythropoietin Treatment. Clearly, it matters which intervention in the league table you are using for comparison purposes. Mason et al. are right to point out that for league tables to be valid, they need to be standardized, such that the same measures of utility, cost and discount rate, are used to calculate the cost-utility ratios. But, standardization does not solve the problem of knowing which intervention in the league table is to be used as the benchmark. Just as important is the fact that even when a benchmark intervention has been identified in the CUA league table, one still does not know whether that benchmark is worthwhile or not. A CBA of the benchmark intervention first needs to be undertaken, in order to know whether being more cost-effective than the benchmark justifies funding the intervention.

3. Cost-Benefit Analysis

The definition of a benefit is an effect that is valued in monetary terms. Because it is expressed in monetary terms, it is then commensurate with the costs, which are almost always measured in monetary terms (For an exception, where costs and benefits are both expressed in non-monetary terms ((time is the numeraire), see the CBA of the 55-mph speed limit in [ 6 ]). It is therefore now possible to compare directly the benefits and costs to see which is greater. If, and only if, the benefits exceed the costs, that is, the difference (called the net-benefits) is positive, then the intervention is worthwhile from the institutional perspective. If the costs and benefits relate to everyone who lives in society, which means that they are social benefits and social costs, then any positive net-benefits indicate that the intervention is socially worthwhile.

3.1. Cost-Utility Analysis as Cost-Benefit Analysis

The effect in a CUA is a QALY (Thus, a QALY is in each individual’s utility function. The social utility is then simply the sum of each individual’s utility function, which is the total number of QALYs for everyone from an intervention). To be designated as a benefit, the QALY must be valued, that is, given a price. In CUA that purports to be a CBA, the price is treated as a constant and is determined independently from the circumstances of the intervention being evaluated. The CUA constant price is a threshold price, usually set at the national level. It is the minimum price assigned to the QALYs in order for the intervention to be judged worthwhile (mainly by others). Often the threshold price is based on some multiple of per capita national income. Ref. [ 7 ] surveyed the literature on the threshold value and suggested a QALY price between USD 100,000 and USD 150,000.

As an example of a CUA used as a CBA, refer to [ 8 ] evaluation of Cholinesterase Inhibitors and Memantine medicines for those with Alzheimer’s dementia. Their results appear in Table 4 . They used the upper limit of USD 150,000 from Neumann et al. as their threshold QALY price. All four monotherapies were found to be socially worthwhile (have positive net-benefits). Since the interventions were mutually exclusive, of the four monotherapies, only Donepezil would have been chosen to be funded.

Net Benefits of Various Dementia FDA-approved medicine interventions.

1 The benefit figures in this column were constructed by taking the QALY estimates for each intervention from [ 8 ]’s Table 1 , and multiplying them by the threshold value of USD 150,000 per QALY.

It is important to understand that the price of a QALY in CBA is primarily meant to be what a person is willing to pay (WTP) for that QALY. This implies that there are two fundamental weaknesses of using a single threshold price to convert a CUA into a CBA. The first weakness, as pointed out by [ 9 ], is that it applies a constant price to a QALY. This is a drawback because in economics a demand curve is drawn based on the principle of diminishing marginal utility. This implies that the price that a person is WTP to pay for a QALY decreases as the more QALYS one consumes increases. Johannesson’s point is relevant to the interventions in Table 4 because Donepezil’s total of 1.6 QALYs was greater than for any of the other interventions. If one QALY is valued at $150,000, but the second QALY is valued at only USD 50,000, then the 0.6 additional QALYs is valued at 0.6 of $50,000 and not 0.6 of USD 150,000 as in Table 4 . The Donepezil Monotherapy benefits would be downsized to $180,000, making the net benefits become $131,824, which is now lower than for Memantine Monotherapy. Priorities could be altered if the assumption of a constant price is invalid.

The other weakness of CUA using a threshold price as the price of a QALY is that it is not based on a person’s WTP. One of [ 7 ]’s justification for the $150,000 threshold came from the World Health Organization’s suggestion that the threshold should be two to three times per capita income, which was around $54,000 in 2014. Using national income as a benchmark is a human capital justification and this is not at all based on an individual’s preferences (The human capital approach used for valuation in CBA in health care assumes that the value of one’s life is the foregone output that society no longer receives because the person dies. The value placed on this foregone output in national income accounting is the price that others place on the products, not what the person whose life is at stake values his or her life).

3.2. CBA Not Based on CUA

Whether one selects Donepezil Monotherapy or Memantine Monotherapy from the list of the mutually exclusive rivals in Table 4 , its social value does not then need to be compared with any other intervention to be justified. The net-benefits are positive and this is the only prerequisite. This is the criterion for any dementia intervention using CBA to be determined to be socially worthwhile. This means that any type of effect that is valid for an intervention can be used in a CBA and it is not necessary that dementia symptoms be standardized in any way as in Table 1 and Table 2 . The effect can be anything that provides value for an individual and society.

In the top part of Table 5 we supply the net-benefits from the CBAs of the five new interventions. Because the effects can be anything that the evaluator considers relevant, the Table is not restricted to the three evaluations that appeared in Table 2 that relied for effects on QALYs (consisting of corrective lenses, hearing aids and avoiding living in nursing homes). Added is education and Medicare eligibility which used the independent living cost savings of reducing dementia symptoms as the effect to be evaluated as in Table 1 . It is true that with different measures of effects, the method used for the pricing of effects would be different. But, the point is that if the valuation method used is valid, because it is based on individual preferences, then any intervention with positive net-benefits is worthwhile irrespective of which other alternative interventions are available (providing that they are not mutually exclusive).

Net Benefits of Various Dementia Interventions.

The pricing method used for the corrective lenses, hearing aids and avoiding nursing homes interventions was based on the Value of a Statistical Life (VSL) literature. Individual preferences are involved in this method because individuals are willing to trade off a specific probability of dying on the job for the extra salaries that are paid per year to compensate for incurring that extra risk. If a person is willing to accept $5000 as compensation for a one-in-thousand chance of dying, then a thousand times $5000, that is $5 million, is what a thousand times greater risk would be worth, statistically speaking. This simply means that, if a population of 1000 persons are working with a one-in-thousand risk of dying, one should expect, on average, one person would be dying for that $5 million aggregate compensation (The $5 million amount was based on [ 10 ]).

The pricing method for the education and Medicare eligibility interventions was in terms of the savings by the effect of reducing dementia symptoms increasing the chances of independent living. When a person can transfer to independent living, caregivers do not have to give up their time and resources looking after the person with dementia. This is true for the government as well as for private citizens as Medicare expenses can go down when people’s dementia symptoms are reduced.

At the bottom part of Table 5 are added two other dementia interventions that did not rely on the NACC data, but illustrate how widespread and multidimensional any dementia intervention can be. Firstly, there is reductions in elder abuse. People take advantage of persons with dementia resulting in psychological, financial and physical elder abuse. This abuse is something that people are WTP to avoid. Using the willingness to prosecute as a measure of this WTP, a benefit amount of between $40,000 to $50,000 was estimated, varying with the type of abuse. By reducing dementia symptoms, one is reducing the extent of elder abuse. Subtracting the cost of $7500 involved with facilitating the prosecution of the abusers, the net-benefits of reducing the dementia symptoms were calculated to be $42,500 (See [ 2 ], chapter 8).

The second intervention added to the bottom part of Table 5 was cognitive rehabilitation. Even when dementia symptoms cannot be reduced directly, the consequences of a person’s dementia symptoms can be mitigated, especially for the benefit of a dementia person’s caregiver. Cognitive Rehabilitation, in the form of the Tailored Activity Program (TAP)—see [ 11 ]—involves an individual specific intervention whereby an occupational therapist comes to a caregiver house, finds out what dementia behavior needs changing, and trains the dementia person to adapt his/her behavior to reduce the caregiver’s time spent “doing things” or spent “on duty” for the person with dementia. Since time saved by the caregiver can be given a monetary value using labor market valuations, for example, by using the federal minimum wage rate, the benefits of the TAP were straight-forward to estimate. The time saving benefits were put at $8875. The occupational therapist’s time spent traveling to the caregiver’s house, and training the dementia patient and caregiver, was estimated to be $942. Subtracting these costs from the benefits made the net-benefits positive at $7933 (See [ 2 ], chapter 9).

The role of Table 5 is to demonstrate the fact that there already exist many dementia interventions that have been evaluated using CBA and found to be socially worthwhile. Many different methods have been employed to put a price on the effect that was found to be the one most relevant by the evaluator of the intervention.

3.3. Social CBA

To be a social evaluation, the outcome measure for the CBA cannot be specific to the healthcare institutional setting. The outcome measure must consist of the effects on everyone in society. Similarly, on the costs side, the costs of everyone affected by the intervention must be summed, including those who incur the funding for the intervention. The relevant economic concept here is that on an externality, where one person’s activities affects some other person and this effect is unpriced. Therefore, pricing of the effects on others should be an integral part of a social CBA. If the patient is considered the first party, and the physician or hospital supplying the service to the patient is the second party, then the externality involves the effect on third parties.

In health care, the third party is often the person accompanying the patient to receive the service. The full cost is not just what is charged by the healthcare provider, it is the transport costs and the value of the time given up by the person accompanying the patient. The full benefit is also wider than the gain to the patient, as the friend or family member receives satisfaction when the patient ‘s functioning improves. This externality was explicitly priced in the context of the cognitive rehabilitation intervention referred to in Table 5 . The value of the caregiver’s time saved by the TAP constituted the net-benefits of the intervention.

In all the CBAs of the new interventions, the effect was the reduction in dementia symptoms that they produced for the patient, and this led to benefits in terms of either cost savings from increased independent living, or from the value of the QALYs. It is important to understand that when a dementia patient’s symptoms are reduced, this will also mean that the dementia symptoms of the caregiver are also likely to be reduced. This is because the spouses of people with dementia are six times more likely to develop dementia than for persons whose spouses have not experienced dementia [ 12 ]. As a result, any reductions in symptoms by the dementia patient will generate external benefits that need to be added to the direct benefits accruing to the dementia patient.

Therefore, all the net-benefit figures for the new interventions in Table 5 must be interpreted to be conservative under-estimates of how socially worthwhile they actually are. The cost-savings from the greater independent living for the caregivers, and the value of the QALYs that they receive externally by the patient’s reduction in symptoms, must be added to the net-benefits of all the new dementia interventions.

Our conclusion that the net-benefits of dementia economic evaluations would likely be higher when externalities are included is supported by the literature. In a survey of 63 CUAs of Alzheimer interventions by [ 13 ], for 33 of them they were able to compare CEA ratios with and without externalities. Of these, 28 (85%) had CEA ratios that were either more favorable, or cost-saving, when externalities were included. The main externalities related to informal caregivers in terms of costs (time savings) and QALYs increases they received. For the subset of CUAs that used a threshold price, and thus were converted into CBAs, 21 (64%) of them that did include externalities did not cross the threshold to make the net-benefits negative (The threshold prices they used to value the QALYs were $50,000, $100,00 and $150,000).

4. Discussion

4.1. health evaluation nomenclature.

Although the previous sections have tried to draw a clear line of demarcation line between CEA and CUA, and CUA and CBA, and even CBA from an individual perspective and CBA from a social perspective, the healthcare literature is not careful to label its published work distinctly. One has to actually read a published paper to know whether it is a CEA, or a CUA, or a CBA. The title of the evaluation paper may not be at all informative. For example, even the Yunusa et al. paper, which we analyzed in this article, that applied a QALY price threshold to its CUA results to convert them into CBAs, did not entitle their paper a CUA or a CBA, but instead called it a CEA.

4.2. CEA as Cost-Minimization

CEA is most valid, and therefore most useful, if it operates in the context of a fixed quantity of an effect. A CEA would then be a Cost-Minimization (CM) analysis. The outcome of the CEA would identify what combination of resources for an intervention would produce a given effect quantity for the lowest cost. Once this has been determined, CBA can take over and see whether the value of the given effect is greater than the minimum cost combination. As soon as CEA departs from CM by considering also differences in the quantity of effects, and compares this to its differences in costs, it loses its validity. For example, antiretroviral drugs for HIV were the least cost-effective of a number of interventions for HIV in Sub-Saharan Africa [ 14 ]; but, when the effect for ARVs was priced to form the benefits, the benefits for this least cost-effective alternative were greater than the costs [ 15 ].

4.3. CBA and Dementia Interventions

Best practice in CBA is to use a person’s WTP as the price of the effect to estimate the benefits (Costs are usually measured by market prices, with this occasionally adjusted for the extra utility loss to taxpayers for financing the intervention by taxation and other externalities (see [ 1 ], part II). For example, for the US at the federal level [ 16 ], taxes have on average an extra utility loss of 0.245 per dollar of taxes raised (averaged over all the elasticity possibilities), making a total loss of 1.245. Thus, for any government intervention that is funded by taxes, the costs must be multiplied by 1.245 to form the social costs. Similarly, for any intervention that provides tax savings, the savings must be multiplied by 1.245 to obtain its social value. In the case of Medicare eligibility, all the costs and benefits are in terms of funds involving the government. Thus, the net-benefits of $1,754,545 in Table 5 would be $2,184,409 when the gain in utility from the tax savings is included). This method can be employed for CBAs in healthcare generally, but can be problematic for CBAs of dementia interventions, seeing that WTP to pay depends on ability to pay, and persons with dementia are rarely engaged in paid employment. For the new interventions, the CBAs relied on more indirect WTP measures.

For the education and Medicare eligibility interventions, it was the external benefits that were estimated. The reduction in dementia symptoms led to the older persons being able to shift back to independent living, which produced cost-savings for caregivers and the government.

For the hearing aids, corrective lenses and avoiding nursing homes interventions, the reduction in dementia symptoms generated QALYs which was priced by using the VSL, which is a WTP measure. Although the older persons were not working at the time of the evaluation, the VSL estimates were based on the risk of dying/ extra salary choices by the older persons when they were last working (the VSL amounts used were related to persons aged 62). For the quantity of life years part of a QALY, it was the life expectancy of the older persons that was applied. For the quality of life part of a QALY, it was the dementia person’s stated preferences that were used, as measured by the Geriatric Depression Scale (GDS). In the NACC data set, 95 percent of the patients were judged by trained clinicians to be mentally capable of completing the GDS. Thus, for the GDS, it made sense to use the preferences of dementia patients to help estimate the benefits for these three new interventions. Whenever an economic evaluation uses a person’s preferences to estimate the benefits, it incorporates one of the central value judgments of CBA, which is to honor consumer sovereignty, that is, individuals are regarded to be the best judges of their own welfare.

5. Summary and Conclusions

In this article the aim was to highlight the strengths and weaknesses of CEA from a societal perspective as a method of economic evaluation in healthcare. This was carried out in the context of a common, single area of application related to dementia interventions. We started off with a narrow focus, where the effect to be evaluated was restricted just to interventions in terms of dementia symptoms. We then broadened the outcome to consider a comprehensive measure, that of a QALY, that can be adopted for the evaluation of any type of healthcare intervention. Using a QALY converts the CEA into a CUA. Finally, from the perspective of a CBA, which supplies the most valid and general method to use for an economic evaluation, one is not at all limited in the type of effect one employs as long as the effect can be priced.

The applications which we used throughout the analysis concentrated mainly on the interventions that were newly evaluated using CBA and found to be socially worthwhile. These were years of education, Medicare eligibility, hearing aids, corrective lenses, and avoiding living in a nursing home. The data used for these evaluations were recast in order that they could be viewed separately as CEAs, CUAs and CBAs. This enabled the contrast between CEA, CUA and CBA to be fully appreciated. For the CUA part of the analysis, we expanded the range of dementia applications to include FDA-approved medicines. This provided a bridge between CUA and CBA, as not only were QALYs used as the effect of the evaluations, they could also be priced and, in the process, form a special type of CBA. This was because a priced effect is what defines a benefit.

What limited the scope of CUAs from the perspective of CBA generally was that they used a single threshold price that was the same irrespective of the preference of the persons who were actually receiving the benefits of the interventions. When the price of effects was not restricted to a single, threshold price, different methods for estimating the benefits could be employed and this allowed the effects for an intervention to be varied as well. Thus, the list of worthwhile dementia interventions was expanded even further to include the prevention of elder abuse and the provision of cognitive rehabilitation. For CBA evaluations, many different pricing methods can be employed and the two methods we highlighted in this article was in terms of cost savings and the value of a statistical life (Note that although a single VSL amount is adopted, which was Aldy and Viscusi’s $5 million, this does not mean that the valuation of a life is a single price for everyone to whom it is to be applied. This is because, for persons at different ages, the remaining life years varies and this make the valuation of a life year individual specific).

When the effect for the CEA is not a QALY, as when the outcome was considered narrowly to be just the reduction in dementia symptoms, CEA would not be able to be compared with any other healthcare intervention that was not related to dementia. Choices by individuals and by the government in economics are always dependent on opportunity costs. If one does not value alternatives to identify the next best alternative, the opportunity cost of an intervention cannot be determined.

Even when CEA’s contribution in the literature was considered greatest, that is, when non-mutually exclusive interventions were considered, and a budget constraint had been assigned, as an economic evaluation method its role is still very limited, as it is always dependent on the particular alternatives that were identified for comparison purposes. This was because how much of the fixed budget that is used up to finance other alternatives always determines how much is left over to fund the particular intervention one is evaluating. No matter how socially worthwhile an intervention may be, if there are no funds left over to finance it, the intervention will not be approved.

The very existence of a budget constraint can be questioned because it predetermines that something will be funded, even without knowing if any intervention for a specific purpose like dementia was socially worthwhile. More generally, the problem with CEA was that even when funds were available, and an intervention was found to be the most cost-effective one, one still had not accumulated enough evidence to conclude that this low-cost intervention should be approved. An intervention can be cost-effective and not socially worthwhile; or it can be the least cost-effective intervention yet, none-the-less, be socially worthwhile.

On the other hand, whether a budget constraint has been specified (or not) does not limit the use of CBA in any way. When a budget constraint exists, CBA chooses the one with the higher benefit-cost ratio. Without a budget constraint, CBA chooses any alternative with positive net-benefits, as this indicates that this intervention is socially worthwhile.

The other main limitation of CEA relates to its practice. In the economic evaluation literature, it is the costs and effects on the first and second parties to the intervention that are primarily considered. The effects on third parties are usually excluded (This exclusion exists even though there was the recommendation in [ 17 ] that states: “All cost-effectiveness analyses should report 2 reference case analyses: one based on a health care sector perspective and another based on a societal perspective”). We emphasized that to be a social evaluation of an intervention, it is the costs and effects on everyone that have be estimated. Externalities need to be included to ensure that it at least becomes a social CEA evaluation. In the context of dementia interventions, it was mainly the costs and effects of the caregivers of the persons with dementia that was the externality that needed to be included.

The main policy prescription that follows from the analysis in this paper is that, when an evaluation is carried out for an intervention in the healthcare field that involves public expenditures, CBA needs to be used as the evaluation method and not CEA. This is because only CBA ensures that outputs will be valued in monetary terms, and therefore made comparable to the costs, to see which is larger, and thereby determine whether the expenditure is socially worthwhile or not. Also, only a CBA provides a social perspective by including the effects on everyone affected by an intervention both directly and indirectly.

Funding Statement

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

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• Research article
• Open access
• Published: 05 September 2018

Cost-benefit analysis of vaccination: a comparative analysis of eight approaches for valuing changes to mortality and morbidity risks

• Minah Park 1 ,
• Mark Jit 1 , 2 , 3   na1 &
• Joseph T. Wu 1   na1  

BMC Medicine volume  16 , Article number:  139 ( 2018 ) Cite this article

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There is increasing interest in estimating the broader benefits of public health interventions beyond those captured in traditional cost-utility analyses. Cost-benefit analysis (CBA) in principle offers a way to capture such benefits, but a wide variety of methods have been used to monetise benefits in CBAs.

To understand the implications of different CBA approaches for capturing and monetising benefits and their potential impact on public health decision-making, we conducted a CBA of human papillomavirus (HPV) vaccination in the United Kingdom using eight methods for monetising health and economic benefits, valuing productivity loss using either (1) the human capital or (2) the friction cost method, including the value of unpaid work in (3) human capital or (4) friction cost approaches, (5) adjusting for hard-to-fill vacancies in the labour market, (6) using the value of a statistical life, (7) monetising quality-adjusted life years and (8) including both productivity losses and monetised quality-adjusted life years. A previously described transmission dynamic model was used to project the impact of vaccination on cervical cancer outcomes. Probabilistic sensitivity analysis was conducted to capture uncertainty in epidemiologic and economic parameters.

Total benefits of vaccination varied by more than 20-fold (£0.6–12.4 billion) across the approaches. The threshold vaccine cost (maximum vaccine cost at which HPV vaccination has a benefit-to-cost ratio above one) ranged from £69 (95% CI £56–£84) to £1417 (£1291–£1541).

Conclusions

Applying different approaches to monetise benefits in CBA can lead to widely varying outcomes on public health interventions such as vaccination. Use of CBA to inform priority setting in public health will require greater convergence around appropriate methodology to achieve consistency and comparability across different studies.

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Health economic evaluations are used to inform medical procurement and reimbursement decisions by public and private healthcare providers. The most popular form of health economic evaluation is cost-effectiveness analysis (CEA), which often presents the ratio of the incremental cost of an intervention (from the perspective of either the healthcare provider or society) to the incremental health benefits of an intervention. A review conducted for the Bill & Melinda Gates Foundation of health economic evaluations of interventions related to malaria, tuberculosis, HIV/AIDS and vaccination in low- and middle-income countries found that, of 204 studies published in 2000–2013, 202 (99%) were CEAs [ 1 ].

Economic evaluations of large public health interventions such as new vaccination programmes attract particularly intense debates because of the high absolute costs (and potentially large benefits) involved [ 2 ]. A major focus of such debates has been about whether current economic evaluation techniques capture the full scope and value of these public health programmes. For instance, several reviews have found that vaccines may have broad, long-term societal consequences that are not always captured in CEAs [ 3 , 4 ], although many of these benefits can, in principle, be monetised and included in CEA based on a broader societal perspective as recommended by the US Second Panel on Cost-Effectiveness in Health and Medicine [ 5 , 6 ]. Such broader, non-health benefits of an intervention include effects on future productivity and consumption, social services, educational achievement and other societal impacts.

Several economists have instead proposed the use of cost-benefit analysis (CBA) [ 7 , 8 ]. The term CBA is often informally used to refer to any analysis used in decision-making that compares the expected costs and benefits (both in monetary terms) of an investment. In principle, to be regarded as complete, a CBA should capture all benefits due to an intervention, valuing them either at their market value or at the level of consumption that individuals are willing to forego to obtain them. Hence, it has its conceptual roots in welfare economics, which quantifies social welfare in terms of individuals’ willingness-to-pay (WTP) to increase welfare. By using a consistent, directly comparable metric to value all outcomes, CBA allows comparison with non-health interventions. A recent analysis estimated that the return on investment (a form of economic analysis that uses the same economic assumptions as CBA) for vaccines in low- and middle-income countries was comparable or superior to that for non-health interventions such as road safety [ 3 ].

The methodology for CEA has been well established, with the perspective or range of costs admissible in a CEA usually prescribed by ‘reference cases’ produced by particular health authorities. In CEAs, the perspective on costs can be narrow (costs and cost offsets falling to healthcare providers alone, as recommended by the National Institute for Health and Care Excellence (NICE) in the UK) [ 9 ] or broad (all costs and cost offsets falling on society, as recommended by the World Health Organization (WHO)) [ 10 ]. NICE’s recommendation to take a narrow perspective when estimating costs is reasonable given that its evaluations are intended to promote the most efficient use of available resources allocated to the NHS (or publicly funded health sectors) in particular [ 11 ]. Conversely, the WHO Guide to Cost-Effectiveness Analysis [ 10 ] explicitly recommended all costs and health effects to be valued from the societal perspective, because there are always opportunity costs in every decision we make, such that all costs and resources used for a chosen health intervention (regardless of who paid them) could have been used for other purposes in society, including non-health consumption. The guide further argued that the so-called ‘decision-maker’s approach’ taking such a narrow perspective is not consistent with WHO’s concern that governments should strive to maximise not only the overall health but also wellbeing of societies. The Second Panel on Cost-Effectiveness in Health and Medicine, composed of experts and leaders in the field of health economics, has also provided two reference cases for healthcare sector and societal perspectives, respectively, in their recent report. The Panel recommended that CEAs are undertaken based on both perspectives to improve the quality and comparability of CEAs [ 5 ].

Each of these approaches also affects the threshold by which an option with a particular cost-effectiveness ratio is deemed cost-effective. The CEA threshold is often determined based on one of the following: (1) the opportunity cost of new spending at the margin of a budget limit, (2) a multiple of GDP per capita, usually based on human capital arguments (although they have also been justified based on WTP) or (3) preference elicitation (based on WTP) [ 12 ]. Using a decision-maker’s perspective and assuming that the decision-maker has control over a budget with the objective of maximising health, the threshold should arguably be set based on the opportunity cost of new spending at the margin of the decision-maker’s budget. From a societal perspective, the threshold should arguably instead be set based on either the human capital value of improved health, or by preference elicitation (based on WTP) of societal willingness to improve health.

In contrast, there is less detail around CBA methodology in health. While there exists guidance on conducting CBAs for government policies [ 13 , 14 , 15 ], it generally is not as precise as ‘reference cases’ available for CEAs that specify the exact economic assumptions to be used in pharmacoeconomic evaluations. This is likely because CBA has not been used as extensively as CEA for informing decisions on specific healthcare resource allocation. While CBA is used to evaluate a broader range of public sector initiatives across multiple sectors, CEA guidelines are generally used in the health sector only.

Given the increasing interest in using CBA to evaluate the value of vaccinations and other major public health programmes (as in part evidenced by the Bill & Melinda Gates Foundation’s recent efforts to develop a reference case for CBA), it is important to understand the implications of different approaches for capturing and monetising benefits. To this effect, we conducted a CBA of human papillomavirus (HPV) vaccination as a case study. HPV vaccination is a major public health investment that has been the topic of numerous CEAs with a total of more than 60 studies identified across a number of systematic reviews [ 16 , 17 , 18 ]. Indeed, vaccination in general has been subject to numerous studies assessing costs and benefits based on various monetisation methods [ 3 , 19 , 20 , 21 , 22 ]. For the current study, we applied eight different approaches to monetise benefits of HPV vaccination and compared the results.

We conducted a CBA of HPV vaccination in the UK. HPV is the aetiological agent of a number of cancers and other diseases such as anogenital warts. Cervical cancer has the highest global burden among the HPV-related fraction of these cancers [ 23 ]. In particular, around 70% of cervical cancers are caused by HPV-16 and HPV-18. We have chosen this example as a large public health investment with a well-established model of HPV vaccine impact used for national decision-making, so that our focus in this study could be on the methodology of CBA rather than on the modelling of HPV epidemiology. For simplicity, we focus only on the value of vaccination in preventing cervical cancer due to HPV-16 and HPV-18.

The decision to introduce HPV vaccination in the UK was informed by a CEA that incorporated an epidemiological model of HPV transmission [ 24 ] to assess the impact of routine female adolescent two-dose vaccination on cervical cancer burden over a time horizon of 100 years. We adopted the same epidemiological model but used it as input for CBA. We assumed that (1) vaccination is given annually to 12-year-old girls at 80% coverage, with a catch-up campaign in the first year to age 16, and that (2) the vaccine provides lifelong protection against HPV-16 and HPV-18 without cross protection against other HPV types. Costs and benefits were discounted at 3.5% per annum. For the probability sensitivity analysis, we used Latin hypercube sampling to generate 1000 scenarios that encompass the uncertainties in epidemiologic and economic parameters.

The outcome in our CBA was threshold vaccine cost (TVC), which we defined as the maximum vaccine cost per person (including the administration cost) at which HPV vaccination has a benefit-to-cost ratio above one (i.e. the vaccination programme is cost-beneficial) (Additional file 1 ). The direct benefits of vaccination included all medical cost avoided due to reduced screening for and treatment of cervical cancer and pre-cancerous lesions (Additional file  2 : Table S1).

We applied two conceptually different approaches to monetise benefits (lost production and WTP) to examine the impact of varying methods on the results. Estimates of WTP were derived from stated or revealed preference studies while lost production were measured using the human capital and friction cost methods as summarised in Table  1 .

Lost production: Conventional production-based approaches

While conventional CBA generally assumes that individuals are the best judges of their own welfare (i.e. consumer sovereignty) and that monetary values should reflect individual willingness to exchange consumption for the outcomes of concern (e.g. [ 25 ], p. 30), lost production has also been commonly used in the CBA literature to value health [ 26 , 27 , 28 , 29 , 30 , 31 ]. Under these approaches, productivity loss averted due to reduction in morbidity and mortality were incorporated as indirect benefits of vaccination in terms of the wider economic effects of health as human capital (rather than its intrinsic value).

We considered the two most commonly used production-based approaches, namely the human capital (HC) and friction cost (FC) methods. From the perspective of affected individuals, the HC method assumes that production loss incurred by sick or deceased workers is irreplaceable. The duration of productivity loss for a sick worker was therefore assumed to be the same as the entire duration of disease treatment, whereas productivity loss due to premature death was estimated by assuming an average retirement age of 65. Specifically, production loss was measured with a cumulative sum of income lost over the duration of illness (morbidity) and the number of years lost due to premature death (mortality) using age-specific employment rates and mean personal incomes retrieved from the UK Office for National Statistics [ 32 , 33 ].

In contrast, the FC method takes the employer perspective and assumes that there always exists some level of involuntary unemployment and hence a sick or deceased worker is replaceable by an otherwise unemployed worker [ 34 ]. As such, the FC method only accounts for productivity loss during the friction period, which is defined as the time between the first day of absence of a sick or deceased worker and the last day of training for a replaced worker. According to the 2015 UK Recruitment Trends Report [ 35 ] based on responses from major UK recruitment agencies, the average time to fill a vacancy (i.e. time between announcing a job and finding a successful applicant) ranged from 6 to 44 days in 2014. The average time in training for a new employee of 6.8 days was derived from the UK Employer Skills Survey 2015 [ 36 ]. As the friction period largely depends on the type of job (e.g. longer friction period for jobs requiring higher-level knowledge and skills) and economic or labour market conditions, it was difficult to find all the necessary data needed to estimate the friction period. We assumed that the sum of (1) the time period between the start of absence by a sick employee and the first day of job announcement and (2) the time period between the acceptance of job offer and the first day of training of a new employee to be approximately 3 to 5 weeks in total based on Koopmanschap’s study [ 34 ]. The friction period in the UK was estimated to be approximately 34 to 86 days. In addition to productivity loss incurred over the friction period, we considered additional administrative costs related to hiring (£2610) [ 36 ] and training (£5433) [ 37 ] a new worker for all mortality and long-term morbidity cases (i.e. treatment time > friction period).

The conventional production-based approaches account for productivity loss from individuals with the paid jobs only and thus disregard homemakers who comprise a substantial proportion of cervical cancer cases (mean age 45, interquartile range 27–59) [ 38 ]. As indicated in one of WHO’s guidelines on CBA, the economic value of unpaid work, such as homemaking and caring, is undervalued using this approach [ 39 ]. As such, we also considered modified versions of the conventional production-based methods (HC-M and FC-M) in which paid labour and homemakers within the same age group were assumed to have the same economic productivity. The assumption is in line with the UK’s recent effort in recognising the value of unpaid work at home and its contribution to the economy, by providing it with a monetary value equivalent to the average wages of those who are paid to do those tasks [ 40 ]. The proportion of homemakers in each age group was approximated based on the Office for National Statistics employment statistics [ 13 ].

Lost production: a new production-based approach

The conventional production-based approach has the advantage that it uses relatively objective and quantifiable measures (e.g. wage rates) compared to a WTP-based approach. However, the theoretical framework of neither the HC nor the FC method is completely sound, because (1) the HC method’s underlying assumption of full employment is often considered unrealistic and (2) the friction period of the FC method largely varies across occupations, times and countries. In order to address both issues, we examined how easily job vacancies could actually be filled within the ‘normal’ friction period in the current UK labour market.

We considered a new approach for estimating productivity loss by interpolating between the HC and FC methods (HC/FC). Under this approach, productivity loss was a weighted average of that under the two methods where the weight for HC corresponded to the proportion of jobs that are unlikely to be filled within the friction period in the current labour market. We estimated this weight based on recent statistics on ‘hard-to-fill vacancies’ (HtFV) from the UK Commission’s Employer Skills Survey 2015 (Additional file  3 : Table S2) [ 36 ]. HtFV refer to vacancies that are difficult to fill due to skill-related (e.g. lack of qualified applicants) or non-skill-related reasons (e.g. low pay offered for the post). It was noted that there is a major gender difference in occupational employment in the UK [ 41 ], with women historically dominating employment in jobs such as leisure and caring while men dominating in construction industry, for example. To take into account the gender difference in occupational employment and largely varying proportions of HtFV by industry sector [ 36 ], we calculated the weighted proportion of HtFV for women to be used in the analysis. We compiled two recent UK employment statistics that provide (1) the distribution of female workforce [ 42 ] and (2) the proportion of HtFV in 13 industry sectors categorised according to the Standard Industrial Classification [ 36 ]. The distribution of females in the workforce largely varied by industry sector, ranging from 0.6% in agriculture to 22% in health and social work, while the proportion of HtFV (regardless of gender) ranged from 23% in education to 43% in construction.

WTP: the value of a statistical life (VSL) approach

Under this approach, the monetary values of both pecuniary (e.g. avoided medical expenses) and non-pecuniary (e.g. pain and suffering associated with the disease) benefits are presumed to be encapsulated by VSL estimates given that individuals’ WTP takes into account the impact of mortality risk reductions on their wellbeing in every aspect. The VSL estimates are used to value mortality risk reductions and obtained via (1) revealed preferences (VSL-RP) based on labour-market or hedonic wage studies; or (2) stated preferences (VSL-SP) based on contingent valuation studies [ 8 ]. While the VSL generally does not address morbidity associated with non-fatal cases, it has been suggested that VSL-RP may as well include the value of the associated morbidity risk though it is likely to be minimal (6–25%) compared to the value of the fatality risk [ 43 ]. As for the VSL-SP, there has been mixed evidence regarding the morbidity premium (or cancer premium) to take into account the effects of morbidity associated with the fatality in the VSL estimate [ 44 , 45 , 46 ]. This highlights a key advantage of the VSL approach over the HC or FC methods that do not take into account the intrinsic value of health gains. We considered seven different VSL estimates derived from three individual studies (labelled as ‘Lang’, ’Viscusi’ and ‘Gayer 1–2’ in Fig.  1 ) and three normative national and international guidance (‘UKHSE’, ‘USDoT’ and ‘OECD’) (Additional file  4 : Table S3). For VSL-RP, we selected (1) a VSL estimate currently in use by the US Department of Transportation (‘USDoT’) [ 47 ], which is very similar to that adopted by the US Department of Health and Human Services [ 14 ] and the US Environmental Protection Agency [ 15 ], as well as the estimated means from a meta-regression analysis, which adjusted for publication bias [ 48 ], and (2) a range of VSL for cancer risk reduction estimated based on hedonic housing prices in the US (‘Gayer 1–2’) [ 49 ]. For VSL-SP, we selected VSL estimates from (1) a WTP study conducted among cervical cancer patients in Taiwan (‘Lang’) [ 50 ], (2) a recent systematic review of VSL focusing on a ‘cancer premium’ (‘Viscusi’) [ 45 ], (3) recommendation of the UK Health and Safety Executive (‘UKHSE’) [ 13 ], and (4) OECD guidelines for EU-27 countries (‘OECD’) [ 44 ]. All VSL estimates were converted to the current UK currency based on the OECD guideline [ 44 ]. To convert VSL values varying across countries and over time to the UK 2015 value, we used the benefit transfer method with income adjustments. For example, to approximate VSL used by the US Department of Transportation, we used purchasing power parity (PPP)-adjusted GDP per capita in the following equation:

Direct and indirect benefits of two-dose HPV vaccination in the UK (top) and threshold vaccine cost (TVC) estimates (bottom) under different cost-benefit analysis (CBA) approaches

Here, PPP-adjusted GDP per capita for both the UK and the US were extracted from the World Bank [ 51 ]. To convert the VSL (in USD) estimated from the above equation to the UK currency, we used PPP-adjusted exchange rates from OECD Statistics [ 52 ]. To update VSL values across different years (e.g. 2000 to 2015), we used the average Consumer Price Index and Real Income in the UK as follows:

Data on Consumer Price Index and Real Incomes across different years were available at the UK Office for National Statistics website [ 53 ]. After the adjustment, the selected VSL estimates ranged from £1.1 million to £7.2 million. Each adjusted VSL estimate was then multiplied by the projected number of cervical cancer deaths prevented from vaccination.

WTP: the quality-adjusted life-year (QALY) monetisation (QM) approach

Under this approach, the health outcome in conventional cost-utility analyses, namely QALY, was monetised using individual WTP for an additional QALY gained. Based on a study that assessed WTP for the respondent’s own additional QALY gained (WTP sel ) in the UK [ 54 ], we applied £23,000 to the discounted QALY gained. Our QM approach with a £23,000/QALY WTP is analogous to NICE’s cost-effectiveness reference case, which has a cost-effectiveness threshold of £20,000–£30,000/QALY [ 9 ], although our approach is based on individual rather than societal WTP arguments. Hence, it would be expected that the net present value of an intervention using our QM approach would correspond to its net monetary benefit evaluated using NICE’s reference case.

Integration of production-based and QM approaches

Under these approaches, productivity loss from production-based approaches and monetised QALYs gained were both included when estimating the economic benefit of vaccination (e.g. HC/QM when HC is integrated with QM) to capture both the intrinsic and the instrumental value of better health. Such analyses are analogous to cost-utility analyses using a societal perspective.

Future deaths averted were discounted at 3.5% per annum back to the reference year, i.e. the year in which the vaccination programme is initiated. Subsequently, the value attached to averted mortality was discounted further, depending on the method used. For production-based (HC and FC) and QM approaches, the productivity loss and QALYs lost for each year of life lost due to premature death was discounted back to the year of death. For the VSL-based approaches (VSL-RP and VSL-SP), the same value was ascribed to a prevented death regardless of the age of the woman or the number of life years averted, as has been standard practice for public policy analyses [ 55 ].

Among all CBA methods considered, the WTP-based approach using the VSL yielded the highest TVC estimates. Specifically, the median TVC estimates ranged from £206 (interquartile range: £187–£223) to £939 (£855–£1021) under VSL-SP and £734 (£669–£798) to £1417 (£1291–£1541) under VSL-RP, which correspond to approximately 78.6% and 541% of the TVC estimated under the standard QM method (£262), respectively (Fig. 1 ). When the QM approach was integrated with the production-based approaches, the TVC estimates ranged from £268 (£244–£293) with FC/QM to £373 (£345–£407) with HC/QM and remained lower than that estimated under the VSL method.

Under the production-based approach, the direct benefit was £0.54 billion (£0.44 billion to £0.66 billion). The mean UK female employment rate used to measure the indirect benefits in terms of averted productivity loss was 36.9% for those aged 16–19 and 64.7–77.6% for those aged 20–64. The indirect benefits varied 9-fold across different monetisation methods utilising the production-based approach, at £33 million in FC, £37 million in FC-M, £946 million in HC, £1.1 billion in HC-M, and £324 million in HC/FC (Fig. 1 ). Consequently, the FC method resulted in the lowest TVC estimate of £69 (£56–£84), which is only 26% of the TVC estimated under the QM. When integrated with the QM method, the total indirect benefits increased by nearly 53-fold (£1.7 billion) and 2-fold (£2.6 billion) for the FC and HC methods, respectively. Similarly, with homemakers (around 8.9%–13.9% across the different age groups in 2015) included in the calculation of productivity loss under the modified production-based approaches, the TVC estimate increased by 1–2% (from £56–£84 to £57–£85) and 8–10% (from £157–£195 to £172–£211) under the FC and HC method, respectively (Additional file  5 : Table S4).

Point estimates and error bars indicate medians and interquartile ranges across 1000 scenarios randomly generated. Benefits under the VSL approaches cannot be decomposed into direct and indirect components. VSL estimates used in Gayer–1 and –2 were derived from the same study using different level of cancer risk [ 49 ].

The proportion of HtFV varied by industry sector, ranging from 23% in education (in which 16% of women work) to 43% in construction (in which fewer than 2% of women work). Considering the gender difference and varying proportions of HtFV across different establishments, we estimated that the overall proportion of HtFV among the female workforce in the UK was 31% (Additional file  3 : Table S2). That is, we estimated that 69% of all vacancies would be filled with a replaced worker within the friction period. The resulting TVC estimate was £101 (£88–£118) under HC/FC, which was 56% lower and 11% higher than that under HC and FC, respectively. Relative changes in TVC were similar when homemakers were included in the calculation of productivity loss.

We found that the economic benefits of vaccination against HPV-16 and HPV-18 in the UK could vary by as much as 20-fold depending on the method used to monetise benefits. In particular, two-dose HPV vaccination in the UK was found to be not cost-beneficial under the HC approach and all FC-related approaches except when integrated with QM.

Our results suggest that using different approaches to monetise benefits can lead to divergent conclusions about the value of vaccination. Our TVC estimates for a vaccine against cervical cancer in the UK ranged over an order of magnitude (£69–£1417) depending on the method used to value the benefits of cervical cancer prevention. The TVC estimate was lowest (£69–£191) when benefits were valued in terms of productivity loss averted due to ill health and premature mortality, particularly if the friction cost method was used, and highest (£206–£1417) when VSL methodology was used. When an individual WTP for an additional QALY gained was used instead, the TVC estimates (£262–£373) were generally higher than that obtained by valuing productivity gains but lower than that obtained using VSL methods.

Our finding that measuring benefits based on WTP estimates (e.g. the VSL and QM approaches) yields larger benefit estimates than measuring benefits based on lost production (e.g. HC and FC) is unsurprising – this is likely because the former includes both financial (e.g. medical expenses and losses in future earnings) and non-financial (e.g. avoided pain and suffering) benefits of the intervention, whereas the latter solely focuses on lost production [ 56 , 57 ]. The finding that the FC method yields much smaller benefit estimates than the HC method is likewise intuitive, because the FC method only takes into account temporary losses during the friction period while the HC method assumes lifetime losses during the entire period affected by morbidity and mortality.

Each of the methods used has advantages and limitations. Production-based approaches for valuing health gains have been criticised for not being consistent with the theoretical foundations of CBA in welfare economics, as they focus on changes in productivity rather than measuring overall welfare. Similarly, QALY-based approaches do not fit naturally within the conceptual framework of welfare economics, because they measure changes in health rather than overall welfare. The approach that most directly reflects the principles of welfare economics is to estimate the consumption that affected individuals are willing to trade-off to avoid morbidity or mortality [ 27 , 58 , 59 ].

Valuing benefits based on averted productivity loss has the advantage of being based on an easily measurable quantity (market income). However, for diseases such as cancer, which tend to cause long-term work absences, the difference in outcome between the production-based approaches can be large. In our study, productivity loss estimates under the HC approach were 29 times higher than that under the FC approach. Similarly, Oliva et al. [ 60 ] found that the annual productivity cost of mortality due to cervical cancer in Spain was €21.7 million based on HC and €0.39 million with FC (56-fold difference). Advocates of the FC method argue that there is always some level of involuntary unemployment, so the HC method overestimates the societal cost of long-term illness or death by measuring the ‘potential’ productivity loss over the entire period of absenteeism beyond the friction period [ 34 ]. The FC method purports to measure the ‘actual’ productivity loss to society from an employer’s perspective by considering the time and related costs (e.g. hiring and training costs) needed to fully restore production levels with a replacement worker.

Both conventional production-based methods have been criticised for valuing life purely in terms of marketable productive capacity and not providing an explicit value for the health gains themselves (i.e. ignoring the additional value of avoided suffering, leisure time and unpaid labour) [ 61 ]. The concern is that this may lead to prioritising interventions that primarily benefit high-wage earners over low-wage earners and those doing unpaid labour (e.g. caregiving and housework). In our analysis, we have accounted for unpaid labour by employing the modified versions (namely HC-M and FC-M) and estimate that the TVC for HPV vaccination increases by 22% with the HC and 2% with the FC method if all females are included in productivity loss calculations (data not shown here), rather than those in the paid labour force only. It should be noted, however, that the market value approach that we used measures unpaid household work based on the population average wage, which differs from the conventional method of valuing household based on the average wage of a paid household worker or carer.

Measuring lost production by using wage rates raises a number of methodological questions, including (1) whether or not to assume full employment (we capture this uncertainty by showing results using both HC, which assumes full employment and competitive labour markets, and FC, which does not make these assumptions), (2) whether the economic value of lost productivity is best captured by the employer perspective (so measured in pre-tax wages including fringe benefits and indirect costs) or employee perspective (so measured in post-tax wages), and (3) how to capture labour market constraints on how much work an individual does, since there may be requirements to work a fixed number of hours [ 62 ]. For example, Bockstael et al. [ 63 ] found that individuals who are required to work a fixed number of hours valued the opportunity cost of time approximately 3.5-fold more than the wage rate, whereas those with flexible working hours valued it similarly to their wage rate.

We proposed an alternative production-based method that may be used instead of established methods, as it strikes a balance between the two approaches in terms of assumptions about unemployment. Weighting the outputs from the two methods according to the proportion of HtFV should theoretically give estimates closer to the actual productivity loss due to ill-health.

An alternative approach is the VSL method. The VSL reflects the marginal rate of substitution between money (or income) and mortality risk and infers the value of the consumption of market goods that individuals are willing to forgo to achieve a reduced risk of premature death [ 8 ]. Hence, VSL can be seen as a direct application of the welfarist principle of consumer sovereignty. Consistent with the conceptual framework for CBA, VSL estimates are highly context specific. In practice, however, researchers often rely on the averages across country populations (or even extrapolations from other countries), which can potentially cause under- or overestimations of the result. Furthermore, there are few VSL estimates from low- or middle-income countries. VSL estimates for cancer are particularly divergent, with debates around the existence and magnitude of a ‘cancer premium’ that inflates the VSL for a cancer death in comparison to a death from an acute fatality to incorporate the latency and morbidity period of cancer. For instance, Viscusi et al. [ 45 ] suggested the use of 1.21 for cancer premium, the US Environmental Protection Agency, the European Commission and several studies recommend a cancer premium of 1.5 [ 46 , 64 , 65 ], and the UK’s Health and Safety Executive doubles the VSL (or the value of preventing a statistical fatality) estimates of accidental death to derive a VSL estimate for cancer [ 13 ]. We understand that there are concerns about transferring VSL between countries with different healthcare systems, income levels and cultural values that may affect mortality risk valuation. Nevertheless, we have used VSL estimates derived from other countries also for the following reasons: (1) there is disagreement and inconsistency with the use of a ‘cancer premium’ when applying a standard VSL to cancer studies and (2) there were few studies reporting cancer-specific VSLs at the time of the study, none of which was from the UK. To minimise such effects, we have adjusted for different income levels and costs between the countries using the ‘unit transfer with income adjustments’ method.

A third approach is to monetise individual WTP for an additional QALY gained. It offers policymakers the flexibility to incorporate additional units for the value of non-health outcomes not captured in measures such as QALYs, as well as to compare outcomes with non-health interventions. In practice, monetised QALYs has been used by government agencies such as the US Department of Health and Human Services [ 14 ] and US Food and Drug Administration for regulatory analyses [ 66 ]. However, there is still an on-going debate around the use of monetised QALYs in healthcare decision-making among health economists. During the meeting organised by the US National Institutes of Health in 2010, for example, it was argued that QALYs should not be monetised since this approach lacks theoretical and empirical support [ 67 ]. It should also be noted that adding productivity costs to monetised QALYs may lead to double counting, as there remains uncertainty about whether productivity loss has been fully captured in QALY measures [ 5 , 6 ].

Hence, a key challenge to using CBA for priority setting around public health interventions is the great variety in the way benefits can be monetised and the relative lack of detail on normative guidance about the appropriate methodology to use.

In principle, CBA offers the opportunity to capture many benefits of public health interventions such as vaccination that may not naturally fit into a CEA framework. Other approaches, such as cost-consequences analysis and multiple criteria decision analysis, also admit a wider range of outcomes, but do not offer a straightforward way to synthesise multiple outcomes into a single measure. Wider use of CBA to evaluate public health interventions will require greater convergence around the appropriate methodology to use in order to achieve consistency and comparability across different studies. Ultimately, discussions around appropriate methodology for CBA could help us better understand what we actually value about health.

Abbreviations

cost-benefit analysis

cost-effectiveness analysis

friction cost

modified friction cost method

human capital

modified human capital method

human papillomavirus

hard-to-fill vacancies

National Institute for Health and Care Excellence

purchasing power parity

quality-adjusted life-year

monetisation of QALYs

threshold vaccine cost

value of a statistical life

VSL based on revealed preference

VSL based on stated preference

World Health Organization

willingness-to-pay

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This study was supported by a commissioned grant (HSK-17-E15) from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region and Award Number U54GM088558 from the National Institute of General Medical Sciences. MJ was supported by the National Institute for Health Research Health Protection Research Units (NIHR HPRUs) in Immunisation at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, the National Institutes of Health, the National Health Service, the NIHR, the Department of Health, or PHE.

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The epidemiological outputs generated from the HPV transmission dynamic model and used in the current study are available from the corresponding author on reasonable request. All other data generated or analysed during this study are included in this published article (and its supplementary information files).

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Minah Park, Mark Jit & Joseph T. Wu

Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK

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MJ and JTW designed and conceived the study. MP collected and analysed the data. All authors contributed to interpreting the results and drafting the manuscript. All authors read and approved the final version before submission.

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Additional files

Additional file 1:.

Supplementary Text. Summary of the literature review and descriptions on the methodology used for calculating conventional and modified production-based approaches

Additional file 2:

Table S1. Summary of cost and QALY parameters used in the model

Additional file 3:

Table S2. Distribution of women in workforce and the proportion of hard-to-fill vacancies across industry sectors

Additional file 4:

Table S3. List of selected value of a statistical life (VSL) estimates included in the analysis

Additional file 5:

Table S4. Threshold vaccine cost (TVCs) based on different methods of monetising benefits

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Park, M., Jit, M. & Wu, J.T. Cost-benefit analysis of vaccination: a comparative analysis of eight approaches for valuing changes to mortality and morbidity risks. BMC Med 16 , 139 (2018). https://doi.org/10.1186/s12916-018-1130-7

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The origin of cost–benefit analysis: a comparative view of France and the United States

• Wei Jiang   ORCID: orcid.org/0000-0002-2231-2844 1 &
• Rainer Marggraf 2  

Cost Effectiveness and Resource Allocation volume  19 , Article number:  74 ( 2021 ) Cite this article

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Cost–benefit analysis (CBA), as a common instrument in the decision making process on how to allocate financial resources, has been widely used in various research areas and in almost all of countries over the world. However, the origin and the historical development of CBA has long been subject to neglect. We attempt to fill this gap and clarify the origin and the early development of CBA.

A comparative analysis is used to investigate the origin and the early development of CBA in France and the USA. The comparison is focused on two questions: (1) which criteria should be applied to decide whether or not a project should be carried out, and (2) with which procedure these criteria can be used for real projects.

The origin of CBA can be dated back to the work of Saint-Pierre in France in 1708. Dupuit introduces the concept of consumer’s surplus that founds the economic basis of CBA. These works are not taken seriously in France and do not draw attention from other countries. Hence, until the 1930s, the principle of CBA is newly proposed in the US and the Green Book marks the mature of CBA.

Conclusions

The early development of CBA in France and the US is independent from the aspects of historical background, personnel, approaches and standardization. This study could help researchers of various disciplines be sure about the history of CBA when they perform this analysis in their research areas.

Introduction

Cost–benefit analysis (CBA) is defined as a systematic cataloguing of impacts as benefits (pros) and costs (cons), valuing in dollars with assigned weights, and then determining the proposal relative to the status quo by the net benefits (benefits minus costs) or the benefit–cost ratio (divide benefits by costs) [ 4 ]. CBA is a decision-aiding tool that quantifies in monetary terms the value of all consequences associated with a government policy (such as setting an environmental standard) or with an investment project (such as reforestation in a floodplain) to all members of society. The purpose of CBA is to help social decision making and to allocate scarce resources more efficiently [ 23 ].

By 2020, CBA has been widely applied to various research areas in almost all countries over the world. Based on the Web of Science Core Collection, the search of cost–benefit analysis in the topic during the period 1900–2020 (performed on September 23, 2021) results in a total of 54,445 publications. The first application of CBA was published in 1951, and studies using CBA have seen significant increase since the 1990s (Fig.  1 a). CBA has been used in 146 research areas, among which the most applications with over 12,000 were found in engineering, followed by environmental sciences ecology, computer science, business economics, energy fuels, health care sciences services, and internal medicine (Fig.  1 b). CBA has been used in 197 countries and regions, among which the most applications with nearly 20,000 were found in the USA, followed by England, China, Canada, Australia, Germany, Italy, Netherlands, and France (Fig.  1 c).

a Publication trend of studies using CBA since 1990s; b Research areas with over 1000 applications of CBA; c Countries and regions with over 500 applications of CBA.

Unfortunately, the origin and the historical development of such a widely used approach has long been subject to overt neglect. There are three opinions about its origin in the literature. Most of researchers take the position that CBA first emerged in the United States during the 1930s (e.g., [ 22 , 28 ]). Hanley and Spash [ 24 ] were the first, and Boland et al. [ 5 ] followed, to claim the earlier origin by Albert Gallatin of the US Secretary of the Treasury in 1808. And, Pearce [ 40 ] and Pearce et al. [ 41 ] attributed its origin to the work of Jules Dupuit in 1844. This makes confusions but indicates that CBA possibly originated in France or the USA.

Therefore, in this paper, we attempt to fill this gap and clarify this confusion by tracing out the origin of CBA and the early development of calculation methods in France and the US, respectively. This study could help researchers of various disciplines be sure about the history of CBA when they perform this analysis in their research areas.

The comparative analysis method is used to investigate the origin of CBA. The comparison is focused on two questions: (1) which criteria should be applied to decide whether or not a project should be carried out, and (2) with which procedure these criteria can be used for real projects. Ekelund and Hébert [ 16 ] and Hufschmidt [ 28 ] offered insightful information on the considerations of both questions in France in the eighteenth and nineteenth centuries, and in the United States at the beginning of twentieth century, respectively. Based on these two studies, we use the snowball strategy [ 29 ] to find more relevant literature, which are then comparatively analysed. In Section “ The origin and development in France ”, we elaborate the thinking and practices of French engineers, while in Section “ The beginning and rise in America ”, we investigate the procedures developed by American professionals. Finally, we conclude with the findings of the comparison in Section “ Discussion and conclusion ”.

The origin and development in France

The idea of a national transportation network in France began in the seventeenth century, since when French engineers worked consciously at introducing a decision rule to guide the construction of public works. The earliest demands depended on French engineers by military considerations, the most prominent engineer of this era was Sébastien Vauban, whose key role was associated to his formative influence on the Corpes des Ingénieurs des Ponts et Chaussées, which was instituted in 1716. A central office within the Corps was established in 1747 for the purpose of training more men and improving their effectiveness, it gradually evolved into the École des Ponts et Chaussées in 1775 and was renamed the École Nationale des Ponts et Chaussées (ENPC) after the Revolution. At the ENPC mathematics was highly appreciated, courses were also given in the engineering subjects of road, bridge and canal building, flood control, harbour improvement, and railroad construction, and economic studies were incorporated into coursework early on. In the first half of the nineteenth century the graduates of the ENPC have been a well-trained group, referred to as Ponts engineers, who developed some fundamental principles and analytical tools to solve cost–benefit calculations of the construction of public works. Jules Dupuit was the culmination among those brilliant engineers in this field [ 16 , 42 ].

The pre-revolutionary period

The first formal cost–benefit study was undertaken in 1708 by the Abbé de Saint-Pierre in measuring the incremental benefits of road improvements. He theorized that incremental benefits would result from increased trade and reduced transport costs. The benefit of increased trade was calculated in two steps. First, the annual value of agricultural output in each province from the annual tax revenue collected was estimated. Then a loss factor, percentage of annual output not produced due to the impossibility of transport, was added to this magnitude. The resulting figure indicated the benefit of increased trade because this loss would be restored by improving the roads. The benefit of reduced transport costs was calculated by the savings per horse and driver. Assuming that the roads were equally passable in all seasons, horses would be able to carry 20 percent more weight, and more trips could be made, only 80 percent as many horses would be required as before the road improvements, resulting in annual savings of 20 percent fewer horses and drivers. On the cost side, the annual additional expenses consisted of costs for administration, repair and continuing maintenance [ 16 ].

From the very beginning of CBA, Saint-Pierre was sensitive to the use of incremental analysis in evaluating public goods. He was also alert to the indirect of secondary benefits by observing that better roads could attract industry and trade, which in turn could increase employment. Additionally, he was well aware that this kind of analytical techniques could be fruitfully applied to all public works. Saint-Pierre framed the important issues that were confronted by generations of Ponts engineers, and his argument was accepted by the engineers of the newly formed Corps [ 16 ].

The era of canal construction in pre-revolutionary France gave the engineers the possibility to turn their cost–benefit calculations to greater effect because the benefits and costs of canals are more precisely economic and more susceptible to measurement. However, due to technical and financial difficulties, the wave of canal construction failed to stimulate much analytical progress. A project for constructing a canal must be certified by engineers according to the public utility of the canal, but the engineers at that time did not understand the concept of demand. Benefits could neither be calculated by increased trade nor by reductions in transport costs, because no trade had existed before. Therefore, benefits were identified chiefly in terms of value of time saved in the shipment of goods, which was notoriously difficult to deal with. Two decision rules emerged eventually to solve these difficulties. First, a canal produced net utility when the resulting savings in transport costs were greater than its construction costs, and second, a canal adds utility when, treating construction costs as sunk, its toll revenues exceed its maintenance costs. But the appropriate level of tolls posed another vexing problem because any levy reduced the public utility of canals [ 16 ].

The revolutionary period

The revolutionary spirit made all past institutions suspect, including the Corps and the École, so that the old regulations in relate to public works were in part abolished, in part abandoned, and altogether dependent on local conditions. After ascending to power Napoleon Bonaparte helped restore the order to the administration of the Corps and began to assign many new projects, most of which were driven by political or military considerations. He was mainly concerned with cost and speed of project construction, therefore, the engineers’ attention was focused on cheapness and expediency rather than on calculating expected benefits. The problem of minimizing transport cost had been solved mathematically by Gaspard Monge in 1776, but this was not a complete solution to the problem at hand because it shed no light on the issue of benefits. It is just on the benefit side that real progress should be made [ 16 ].

The post-revolutionary period

The engineers were recentralized in the Ministry of the Interior during the Restoration, making a fertile period in economic analysis from the 1820s to 1840s. The stimulus given to canal construction prompted a number of minor advances in the formulation of CBA.

In 1822, focusing on the value of time saved in transport and the amortized costs of building and maintaining a canal, Pierre-Simon Girard tried to measure the benefit of the canal in physical terms by employing a curious combination of hydraulics and economics. The shortcomings of his method were demonstrated by Louis-Joseph Favier in 1824, who established the principle that a public work could be justified when it conveyed positive net utility, that is, the amount of net revenue from the public work must be greater than the cost of (re)construction, disregarding how the revenues and costs were assigned. To emphasize the choice between alternative public investments Favier derived a rule stating that a public work is to be preferred if its net utility exceeds that of another, taking into account the life of the respective constructions and amortized costs [ 16 ].

As early as 1830, a prominent Ponts engineer named Henri Navier set up a cost–benefit principle that public works should be provided only if the total benefits exceeds the total costs by measuring the benefits of new transport facilities based on an estimate of cost savings. His effort produced a decision rule that allowed for calculating minimum demand for new public works, below which the construction would be against the interests of the state. Algebraically, Navier defined the annual recurrent costs related to a new canal ( C ), the price of goods transported by road ( r ), the price of goods transported by canal ( c ), cost savings to consumers attributable to the canal ( S  =  r   −   c ), and the annual amount of goods transported on the new canal ( n ). Since n is a function of S, there must be some n ′ and S ′ such that n ′ S ′ =  C , so n′ is the minimum demand being sought. If n  >  n ′, the state would gain annually a net amount equal to S times ( n   −   n ′), while if n  <  n ′, the state would lose annually a similar amount [ 13 , 15 , 16 ].

Joseph Minard, who was an important link between Navier and Dupuit in the development of CBA, made two major advances to Navier’s analytical framework in 1832. First, he recognized that utility-increasing cost savings resulted from changes in consumption by inducing old consumers to substitute the lower-priced good for other goods and by drawing into the market new consumers who could not afford the good before. When new consumers entered the market, the utility gained by society would depend on consumers’ reaction to this price change, which in turn would depend on the consumers’ income. Second, in comparison to Navier, Minard introduced more subjective elements into the measure of benefit. A unique one was his explicit treatment of time. He insisted that time must be given a value, and that failure to take the benefits from time saved into account would lead to systematic underestimate of social benefit. However, he was clearly aware of the difficulty in evaluating the time, and he overcame this hurdle by assigning a subjective monetary value to the time, for example using wages as the opportunity cost of a worker’s time [ 13 , 15 , 16 ].

In 1833, Charlemagne Courtois developed a single principle for the selection of the most preferable transport project linking two cities. That was to choose the project that, given the costs, provided the greatest benefit. To identify the benefits of a project, Courtois considered as relevant variables the amount of goods in tons ( n ), the transport costs per ton and kilometer ( p ), the distance of the route ( l ), a sum of outlay ( A ), and the construction and maintenance costs ( C ). His analysis distinguished between communications already existed and new ones to be established. In the first case, he argued that the most preferable project should be the one over which, for a given outlay A , the greatest amount of goods could be carried. Since A  =  nlp , he took n =  A / lp as the typical form of the solution and concluded that to the least product of l and p would correspond the greatest value of n, and consequently the most preferable project. In the second case, the construction and maintenance costs should be taken into account. Courtois introduced the ratio of the amount of goods carried per unit of construction and the maintenance costs as the benefit criterion which he called “the measure of advantages”. He took n / C  =  A / Clp as the typical form to determine the character of the project with the maximum of advantages [ 16 , 46 ].

André Mondot de Lagorce adopted and improved these considerations by more rigor and generality in 1840. He treated costs more sophisticatedly by normalizing annual maintenance costs in terms of the average costs of labor and materials, and he was keenly aware of the difference between the interest rate as a cost of capital and the discount rate used to reduce future expenditures to present value. Mondot realized that a full solution required estimating transport demand on the new route, and admitted that it was impossible to determine the exact demand a priori, because demand depended on the choice of projects, which was the solution being sought. But he refused to abandon economic calculation, insisting that the estimation of demand, despite imperfect, was better than arbitrary decision [ 16 ].

On the cost side, Mondot defined the construction costs ( c ), the annual maintenance costs ( d ) normalized according to average costs of labor and materials, the appropriate discount rate ( r ), and the annual savings in maintenance costs on the old road ( S ) owing to less traffic after the new road is built. Thus, the annual expense of the new road ( C ) is equal to cr  +  d   −   S . On the benefit side, he defined the average transport cost on the new road ( p ) calculated as a function of weight, the average transport cost on the old road ( q ), and the estimated amount of goods to be transported on the new road ( n ). Thus, the total benefit of the new road ( B ) is equal to n ( q   −   p ). For the value n was not given, Mondot proposed the measure of “the real utility per unit of expenditure”, which is equal to ( q   −   p )/ C , as “the administrative value” of a project. What he called a “normal project” was the one with the highest “administrative value”. Mondot’s work typified the simple definition of CBA by proposing a criterion that compared disadvantages ( C ) with advantages ( B ). So long as a large number of projects were desirable, rigorous estimate of transport demand ( n ) on new routes was not always a pressing problem, for one could simply reject all projects for which B   −   C was not sufficiently positive [ 16 ].

However, this straightforward approach ignored important demand effects that resulted from the reduction of commodity prices induced by lower transport costs. A more sophisticated solution required a theory of demand. In 1844, Jules Dupuit published his breakthrough article “On the measurement of the utility of public works”, not only providing the demand function derived from a basic theoretical principle of consumer behaviour, i.e., marginal utility, but also introducing a practical measure of economic welfare, i.e., consumer surplus, which became the theoretical basis of CBA and stood as lasting monuments to the pioneer efforts of the French engineer-economists [ 16 , 45 ].

By raising the question of how the utility of public works was to be measured, Dupuit began with the definitions of utility. Using the examples of wine tax (market good) and water system in a town (public works), he came to the conclusion that “each consumer himself attaches a different utility to the same thing according to the quantity which he can consume”. Then he distinguished between the absolute utility and the relative utility. “In general, the relative utility of a product is expressed by the difference between the sacrifice which the purchaser would be willing to make in order to get it, and the purchase price he has to pay in exchange”, supposing that the market price of the product is more or less equivalent to the costs of production. Although Dupuit did not identify the exact concept of marginal utility, he did illustrate the idea from an empirical consideration that Ponts engineers typically confronted, and concluded that “in general every rise of fall in price decreases or increases utility by an amount equal to this variation for those who are consumers in both situations; for those who disappear or who appear, the utility lost or acquired is equal to the old or to the new relative utility yielded to them by the product” [ 11 , 14 , 36 ].

By pointing out the error in Navier’s calculation of the utility of a canal, Dupuit proposed his method, arguing that the measure of utility for products already being consumed should be based on reduction in costs of production rather than reduction in costs of transportation, while in the case of new commodities being transported the measure of utility would be the lowest tax which would prevent their being carried by the new route. To operationally calculate the utility of public works, Dupuit derived his “consumption curve”, which was actually the marginal utility curve (Fig.  2 ). He defined the “consumption curve” as q  =  f ( p ), thus placing the independent variable (price) on the x axis and the dependent variable (quantity) on the y axis. He showed that the absolute utility of Oq ′ articles is equal to the area Oq ′ n ′ P under the consumption curve, and derived the relative utility, what is now called consumer surplus, by subtracting the costs of production shown as Oq ′ n ′ p ′, which leaves the area n ′ p ′ P . Suppose the price decreases from p ′ to p due to a reduction in costs of production, so that the quantity consumed increases from q ′ to q . This raises the absolute utility to OqnP , subtracting costs of production Oqnp from this amount yields the relative utility of npP , so the net gain in relative utility is measured by pnn ′ p ′ [ 11 ]. In this manner Dupuit not only developed a monetary measure of the benefit of public works and of goods in general, but also forged the most important tool of welfare economics. It was a significant breakthrough, but clearly far from perfect.

Dupuit’s “consumption curve”

We summarize the contributions to CBA made by French engineers in Table 1 . Unfortunately, the methods for calculating benefits proposed and improved by the engineers did not form general standards or decision rules that were accepted on the administrative level to value a single project or to justify the choice between rival projects. Until the end of the nineteenth century the administrative form of economic quantification in project planning was still carried out in terms of cost and revenue, not costs and benefits [ 42 ]. However, on the other side of the Atlantic, CBA was emerging and would see its real rise in the United States of America.

The beginning and rise in America

The American water resources development, including navigation, flood control, irrigation and water power, initiated from the beginning of the nineteenth century [ 20 , 49 ]. The Gallatin Report of 1808 proposed partly a nationwide system of canal and river improvements justified on the basis of economic development of the west, political unity and national defense needs, but it had no immediate effect because it was only a statement about the issue of public investment in transportation and far from an economic analysis of individual projects [ 25 , 27 ].

As the first major construction agency with the requisite technical abilities, the Army Corps of Engineers established officially in 1802 on the model of the Corpes des Ingénieurs des Ponts et Chaussées, was given the responsibility for planning river and harbour improvements in 1824. The establishment of the Mississippi River Commission involved the Corps in flood control in 1879. The 1902 River and Harbor Act created a national-level Board of Engineers within the Corps to evaluate construction and maintenance costs, commercial benefits and necessity of river and harbour improvements. The 1917 Flood Control Act introduced the principle of local financial contributions to flood control, and authorized the Corps to undertake comprehensive studies of watersheds regarding the relationship of flood control to navigation, water power and other uses. The 1920 River and Harbor Act further required the reporting of local benefits for the recommendations of appropriate local cost sharing [ 1 , 27 , 47 ]. American efforts to economic evaluations of public investments during this early era were lack of rigor and depended almost completely on estimate [ 25 , 42 ]. The Report on the Chesapeake and Ohio Canal made in 1826 is a representative example (Table 2 ).

The beginning of CBA

The modern economic analysis of project value began during the New Deal. The most important agency in relation to water resources in this period were four successive national resource planning organizations operating between 1933 and 1943, namely National Planning Board (NPB, 1933–1934), National Resources Board (NRB, 1934–1935), National Resources Committee (NRC, 1935–1939), and National Resources Planning Board (NRPB, 1939–1943) [ 10 ]. With the most quoted passage “…if the benefits to whomsoever they may accrue are in excess of the estimated costs…” [ 48 ], the 1936 Flood Control Act is usually considered as the beginning of cost–benefit analysis in the United States [ 8 , 22 , 28 , 40 ]. However, the outstanding report of the NRB in 1934 has the larger authority on the emergence of CBA for three reasons. First, like the famous act, this report had a clear statement that “we hope in general to achieve rational planning and in particular to achieve equitable allocations of benefits and contributions to cost in public works programs”. Second, it identified tangible, measurable intangible, as well as immeasurable benefits. Third, it included substantial economic basis. Additionally, two principal categories of water projects were recognized, one for income producing e.g., hydroelectric power and another for loss preventing e.g., flood control, which implied that prevented tangible and intangible losses are the measure of benefits [ 37 ]. Nevertheless, the 1936 Flood Control Act still has significant meaning that a strict cost–benefit rule is written into law and hereafter Congress can only, without exceptions, authorize projects that have been studied and approved [ 42 ].

The 1938 report of the NRC suggested that social and economic benefits, general and special benefits, potential and existing benefits should be taken into account in deciding whether or not large water projects should be undertaken as well as in distributing the costs of projects among the beneficiaries [ 38 ]. The 1941 report of the NRPB further recognized two general categories—tangible and intangible—of benefits and costs as well as two types—primary and secondary—of tangible benefits and costs. It suggested that a project plan was economically sound if total benefits were greater than total costs, and benefits from each function of multiple-purpose projects were greater than separable costs incurred solely in serving that function [ 39 ].

The Water Resources Committee of the NRPB and its predecessors contributed greatly to the development of CBA. Although much of its contribution was not highly technical and far less than complete [ 10 ], the committee’s works were so basic and influential that they would consistently find retrospective in the postwar history of CBA. However, CBA itself was only an administrative device owing nothing to economic theory in this initial phase [ 21 ] and did not become the principal basis for project evaluations of related agencies until the 1950s.

The issuing of the Green Book

Besides the Corps, there were many other agencies involved in water resources development, however, each agency adopted different and inconsistent methods of estimating costs and benefits. The 1941 report drew attention to these inconsistencies and advocated cooperative studies to develop uniform methods. After the NRPB was abolished in 1943, a new pattern of coordination arose with the establishment of the Federal Interagency River Basin Committee (FIARBC). In 1946 a subcommittee on benefits and costs was appointed “for the purpose of formulating mutually acceptable principles and procedures for determining benefits and costs for water resources projects”. This subcommittee issued a final report entitled “Proposed Practices for Economic Analysis of River Basin Projects” in 1950, which became known as the Green Book [ 27 , 42 ].

The Green Book is recognized as the first landmark in the history of CBA in the United States [ 27 , 28 ]. CBA covered completely for the first time its modern subjects, including definition of benefits and costs, general procedure for the measurement, consideration of available alternatives, criteria for comparing alternatives, choice of discount rate, risk allowances, and economic life of projects. One of the strengths of the Green Book lies in stating the basic principles of microeconomics, although not in highly theoretic terms. It stated that the ultimate aim of water resources development projects is to satisfy human needs and wants by providing goods and services, which refer to all objects and activities that have the power of satisfying human needs and may increase or decrease in availability as a result of a project. It was aware of the limitations of the market price system in reflecting values of goods and services from a public viewpoint, but concluded that there is no more suitable framework for evaluating public projects in common terms. Therefore, market prices had to be chosen as the starting point for measuring the tangible effects of a project, whether benefits or costs. Some tangible effects that cannot be assessed based on market prices may be derived indirectly from prices for analogous effects or from the most economical costs of producing similar effects by an alternative means [ 18 ].

Another advantage of the Green Book is to apply these principles to develop operational procedures for quantifying benefits of various project purposes, such as irrigation, flood control, navigation, electric power, and recreation, although not in sufficient detail to serve as a manual. For example, the primary benefits of flood control should either be measured in terms of the estimated costs that would be avoided with flood control but would be incurred without it, or be evaluated as the costs of repairing or rehabilitating the affected property. Measuring primary benefits from water power was based on the costs of equivalent power from alternative sources that would most likely be utilized in the absence of the water power [ 18 ].

Here, all of cost-based methods that are used widely today, namely avoided cost method, restoration cost method, and replacement cost method, have their rudiments. Unfortunately, the Green Book argued that the benefits of navigation improvements were measured by savings in transportation costs rather than reduction of production costs, which was Dupuit’s main point before one hundred years. For the purpose of evaluating recreational benefits, the Green Book mentioned two approaches being used at that time [ 18 ]: recreational benefits were assumed either to be equal to the sum of expenditures by recreationists for items like gasoline, lodging and equipment (expenditure approach), or to be equal to the costs of installing, operating and maintaining specific recreational facilities plus an equal amount that was considered to be the value of the benefits attributable to recreational use of facilities provided for purposes other than recreation, suggesting that recreation benefits were equal to twice the specific costs (twice-cost approach).

The bureau of reclamation and the bureau of the budget

Although the Green Book had considerable influence, it failed utterly to reconcile the cost–benefit practices of relevant agencies, especially the Bureau of Reclamation (BOR). The BOR, established by the Reclamation Act in 1902 for the purposes of making examinations and constructing irrigation works, was the most important rival agency against the Corps in this field. Since the BOR was the specialist on irrigation, it created a set of discrepant methods for quantifying the benefits of irrigation, which contained extravagant measures of indirect benefits. Possibly because the BOR was not involved in preparing the Green Book, the report took gentle but clear position against the Bureau on the issue of secondary benefits, stating that secondary benefits should only be considered under certain strict conditions. It was no surprise that the BOR did not accept these restrictions [ 28 , 42 ]. In order to resolve this issue, the BOR called on a Panel of Consultants to indicate the adequacy of existing procedures for evaluating secondary benefits, and to set forth a recommended basis for their evaluation. The report of the Panel in 1952 stated that secondary benefits were much less determined and measurable than primary, and depended more on far-reaching hypotheses. Usable formulas could not be based on data that are capable of affording accurate and complete comparisons of effects with and without a given project. Thus, it recommended that primary and secondary benefits be separately shown in benefit–cost ratios [ 9 ].

Beginning in 1943, the Bureau of the Budget (BOB) was required to review and consolidate all public works including water resources projects. Attempting to supervise the economic justification of projects, the BOB in 1952 issued Budget Circular A-47, which was in many respects similar to but more restrictive than the Green book, to set forth uniform standards and procedures in reviewing proposed water resources projects [ 27 , 42 ]. The Budget Circular A-47 based the evaluation mainly on primary benefits, and provided that not only must the total benefits of a project exceed its costs, but the benefits attributable to any purpose of a multi-purpose project must exceed the costs of including that particular purpose. Additionally, it proposed clearly for the first time that increases in the values of recreation and fish and wildlife resources as a result of the project were a category of primary benefits to be included in evaluation [ 7 ].

The early work on water resources evaluation until the early 1950s was undertaken by professionals from federal agencies (Table 3 ). Many of these professionals had had a bureaucratic as well as training identity that they worked in the Bureau of Agricultural Economics. However, academic economists relevant to CBA outside the bureaucracy did not almost exist in the early 1950s. There was very few papers published in economic journals on the economics of public investments, and the work of these agricultural economists on the benefits of public works was more closely related to a bureaucratic discourse than an academic one [ 2 , 28 , 42 ].

Along with the consolidation of welfare economics in the 1950s [ 19 , 32 ], CBA became quickly an attractive area for academic economists since the late 1950s. Two important institutions during this period, the Harvard Water Program and the RAND Corporation in California, generated an extensive literature of systematic studies [ 12 , 26 , 31 , 33 , 34 ]. Taken together, these books set a sound microeconomic and related welfare economic foundation for the theoretical and applied aspects of CBA. The various market-based valuation methods introduced in the Green Book were firmly established. In addition, some difficult conceptual issues such as externalities, opportunity costs, consumer surplus, and secondary benefits that were not familiar to or had troubled earlier practitioners were discussed and clarified [ 22 , 28 ].

In the 1960s, the applications of CBA had widened from water resource projects to almost all kinds of government activity, such as public health [ 50 ], transportation [ 35 ], education [ 6 ] and urban renewal [ 43 ]. By this time, CBA had become not only a standard tool for the analysis of government expenditures, but also a legitimate branch of welfare economics [ 22 , 42 ]. A still more significant breakthrough made by economists lies in the attempt to value recreational benefits, which leads to the flourishing developments of various environmental valuation techniques.

Discussion and conclusion

The origin of CBA can be dated back to the work of Saint-Pierre in 1708. Through the efforts by a number of French engineers, Dupuit introduces the concept of consumer’s surplus in 1844 that founds the economic basis of CBA and measures benefits in terms of the reduction of production costs. However, these works are not taken seriously in France, and do not draw attention from other countries. Hence, the principle of CBA is newly proposed by the 1936 Flood Control Act in the US, and the Green Book in 1950 marks the mature of CBA by establishing cost-based methods for measuring benefits.

The comparative analysis shows that the early development of CBA in France and the US is independent from four aspects. First of all, there is no considerable evidence suggesting that the American experts are familiar with the early works of French engineers. Although Charles Ellet Jr., an American civil engineer, indeed traveled to Paris in 1830 to study as an external student at the École des Ponts, his contributions to the economic thought lie mainly in the practical problem of monopoly profit maximization of a railroad rather than CBA [ 16 , 42 ]. The cost–benefit criterion proposed by Navier and Mondot finds no mention in the American documents. And the Green Book published in 1950 still considered the benefits of navigation improvements as savings in transportation costs rather than reduction of production costs.

Second, the backgrounds for introducing CBA in France and America are clearly different. The French tradition rooted in the field of transport economics, whereas the American tradition was related to water resources projects. Third, the personnel who made efforts to the development of CBA are also distinct. French engineers have formal academic background, but American professionals work in federal agencies. Both facts result in that French engineers follow a theoretical approach and attached great importance to mathematical calculation, while American professionals adopt an empirical approach and pay more attention to practical application [ 30 ]. This divergence in approaches leads to the theoretical foundation of consumer’s surplus in France and the practical guidelines of CBA in the United States, respectively.

Finally, the most crucial difference lies in the attempt for standardizing this approach in America, but such a progress does not take place in France. The political rival situation among agencies with overlapping responsibility is the major driving force for the standardization in the US [ 42 ], but the French Corps has a strong administrative, institutional and legally acknowledged monopoly position, which prevented the standardization. Another reason could be the lack of incentive within the power-conscious elites, the active thinking exchange among French engineers through the internal journal generate a variety other than a unity of measurement suggestions. More important, different from the practical considerations of American agencies, for which a pure quantitative economic excess of benefits over costs is necessary and sufficient, French decision-makers at that time also consider unquantifiable variable such as security, reliability, or even promotion of administrative centralization [ 17 , 44 ].

Availability of data and materials

All data generated or analysed during this study are included in this published article.

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Jiang, W., Marggraf, R. The origin of cost–benefit analysis: a comparative view of France and the United States. Cost Eff Resour Alloc 19 , 74 (2021). https://doi.org/10.1186/s12962-021-00330-3

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Impact and cost–benefit analysis: a unifying approach

• Pasquale Lucio Scandizzo   ORCID: orcid.org/0000-0002-8824-3589 1  

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This paper presents a methodology to integrate cost–benefit analysis and SAM-CGE-based impact evaluation. While the two types of analysis have developed in parallel and without a clear connection, there is growing consensus that the two approaches should be integrated for complex investment projects, since their economic evaluation cannot rely solely on the partial equilibrium assumptions of cost–benefit (CB) analysis. Unlike CB analysis, impact evaluation looks at the economy as a complete system of interdependent components (industries, households, investors, government, importers, exporters). By integrating project accounting into a SAM-CGE framework, the methodology developed presents several properties that make it fit to the purpose of providing a reliable assessment of project contribution to the economy.

1 Background

A social accounting matrix (SAM) is an analytical construct that brings together the material input output matrix originally conceived by Leontief, with a consistent framework of transactions across production sectors, factors of production and various classes of economic agents. In the form that has become more popular and was developed first by Stone’s research team in Cambridge, the SAM is the basis of modern national accounting and records transactions across activities, production factors, and the main institutional agents that constitute a modern market economy, namely households, enterprises, government, the financial and the international trade sector. As for the input–output, but in more integrated and value-based fashion, a SAM collects data on inflows and outflows of services by recording receipts and payments using the double accounting principle in a mutually consistent form. By convention, each column of a SAM thus records payments from that account to each other account, while each row records receipts to a specific account from each other account, with total receipts being equal to total expenditures. For this reason, the columns reporting production activities outlays can be interpreted as production processes, whose costs, including factor payments, equal in aggregate the value of production. At the same time, the corresponding rows can be interpreted as the revenues of each activity from all other activities. The SAM records value added formation by accounting for payments from activities to primary production factors (capital and labor) and to the government in the form of indirect taxation. The SAM also accounts for income formation by recording payments from factors of production (value added) to households, government and other institutions, including the financial and the foreign sector, and by accounting for other transactions, including direct taxes, subsidies, interest payments and various types of transfers across institutions. The SAM concept and practice is at the base of the UN national account system (SNA), and in more recent years it has been extended to include environmental flows, such as carbon emissions and ecosystem services of various sort (Burthoo-Barah et al. 2019 ; Scandizzo and Cufari 2019 ; Scandizzo and Ferrarese 2015 ).

While the SAM has been often utilized to evaluate the indirect effects of capital expenditures from investment projects as a demand shock (Scandizzo and Ferrarese 2015 ; Debowicz et al. 2013 ), its use to assess the impact of the investment beyond the activation effects of the construction period, has been limited. The theory and practice of project evaluation has also typically neglected the fact that, while in the construction period a project may be considered as part of the capital formation account, after the construction period, the project is generally a production activity, which is linked to sectors, factors and institutions by a series of transactions that can be accounted for, using the same principles, industrial classification and balancing of all other activities represented in the SAM. For example, the increased demand generated by public investment in a new road during the construction period can be measured as a vector of purchases of capital goods, in analogy and as an implicit part of the investment (capital formation) vector in the SAM. Once completed, however, the road becomes a service producing activity that not only generates increased demand for inputs, but also contributes to revenue formation, increase consumer surpluses and spill over the economy thereby increasing economic activity in the rest of the economy. An investment project, in particular, is characterized by a cash flow, for each year or project life, that can be split into a series of costs and a series of revenues and, as such, conveniently represented in a specific column and row of a SAM. This representation not only provides a convenient way to represent a project in a SAM, but also suggests that SAM production activities can be seen as sets of investment projects at various stages of implementation and are thus subject to variations over time due to the overlapping of projects of different generations. In turn, this twofold consideration on the role of projects as activities and of the activities as overlapping ongoing projects, indicates that a proper SAM accounting should not only aim at accounting for direct responses to final demand changes, but also for the variation of the patterns of transactions (i.e., changes in the SAM coefficients) due to new projects and technologies.

Following Stone’s original interpretation (Stone 1952 ), SAM accounts and coefficients can be considered special cases, respectively, of a transaction and a response matrix for an economy. The response matrix can be more or less complex and may coincide with a linear approximation of a full-fledged computable general equilibrium model (Perali and Scandizzo 2018 ). In the case of an investment project, therefore, while the project cash flow can be directly incorporated in a transaction SAM, its translation into a column and row vector of coefficients require a series of assumptions on the technology and the behavior of the economic agents involved. In the simplest case, the response matrix is a matrix of input output coefficients for the production activities, and of expenditure shares for the behavior of the institutions (households, government, etc.). In the case of a CGE model, the project transaction column contains, for each period of project life, the project payments to the factors of production (including the project net revenues to capital), as well as to activities and institutions. The response (coefficient) column instead in the simplest case embeds the hypothesis that project outlays are fixed proportions of its total cost. Similarly, the project transaction row contains the revenues and possibly the external effects paid to the project by production activities, institutions and the financial sector. For these stakeholders the payments made to project represent costs for goods or services provided by the project, so that in the response matrix, they are converted into coefficients, which in the simplest case are fixed proportions of total costs of their own production. As required for all SAM accounts, project rows and columns of the transaction matrix balance, since the project outlays, which include the net project revenues paid to capital, equal the project inflows or gross revenues with any financial gap filled by the capital formation (i.e., the financing) sector. For example, in the construction period, the outlays will consist of the construction costs, while the balancing inflows will be provided by payments from the capital formation accounts.

More generally, the rows and columns of the SAM as a transaction matrix can be reinterpreted as the twin entries of the cash flow account of an economy for a given period of time. For the production sectors, this means that revenues from product sales are recorded in the row entries and costs in the column entries. The difference between revenues and intermediate costs is value added. This can be considered a result of the production activity (the larger the better, ceteris paribus) and is the basis to compute all welfare measures including GDP. In turn, value added is recorded as a row entry in the accounts of factors of production. Here row entries represent incomes from employment while column entries document payments to factor owners, which in a market economy are households, firms and the government. For institutions, finally, row entries represent incomes from factor ownership, interests and dividends, or transfers of various kind, while column entries are expenditures for goods and services and savings (as purchases of financial assets). Rather than value added, however, the return to capital in the form of gross margins of production are recorded as the difference between sector revenues and sector costs (including cost of intermediates and cost of labor) and are credited to the account of capital as a factor of production.

In sum, while the general practice in input output and SAM-based model has been to consider an investment project as a vector of expenditure shocks, the proper way to analyze it is as a special form of an activity, with its own input output parameters that evolve over time. This type of activity is characterized by a series of cash flows that change over time. At any point in time an investment project can thus be represented in the SAM as a column of cash outflows, including all capital and maintenance costs from intermediates and resulting value added, and as a row of cash inflows, including financing from the government and private savings during the construction phase, and revenues from increased production of goods and services during the operational phase. Since costs and revenues have to balance, financing from the capital formation sector, or directly from the government or other project sponsors must be recorded as one or more row entries in the years where cash outflows are larger than cash inflows (the “construction” period). Vice versa, once the project is operational and inflows become larger than outflows, returns can be credited to capital (as gross business margins) or institutions (government or households).

The benefits from the project, however, are not limited to the remuneration of capital, since other social benefits and costs may also be considered in the economic analysis.

2.1 A SAM-based model for project evaluation

Consider the social accounting matrix equation for a generic scenario:

where X is an n ,1 vector of activity levels for productive sectors, and incomes for factors and institutions and \(Q = I - A\) , the SAM coefficient matrix.

We can consider an investment project as an additional activity and augment the size of the SAM by adding a column and a row of transactions corresponding, respectively, to the outlays and the receipts of the project cash flow. In order for the inflows and outflows to balance, this entails, in particular, accounting, among the receipts, for any financing flow and, among the expenditures, any profits distributed to factors of production and other stakeholders. We can then write two new equilibrium conditions for the situation “without” and “with project” SAM as:

In (2a) and (2b), \(A_{s}\) and \(A_{c}\) are n  + 1, n  + 1, SAM matrices augmented of one column and one row to represent, respectively, the situation without and with the project. The matrix without the project \(A_{s}\) , in particular, can either contain an additional column and row of zeros, for the case of full project additionality, or the data of the cash flow of an alternative project as a counterfactual.

Subtracting Eq. ( 1 ) from Eqs. ( 2a ) and ( 2b ), we obtain, after some manipulation:

As noted in the literature on structural decomposition (e.g., Rose and Casler 1996 ), the two expressions ( 3a ) and ( 3b ) signal an index number problem. In the remainder of this paper, we will assume that the differences between (3a) and (3b) are small enough that they can be ignored or otherwise solved by appropriately averaging the results obtained by separately applying the two equations (Koppany 2017 , p. 619).

Both the \(A_{s}\) and the \(A_{c}\) matrix are singular, but we can decompose them into a non-singular square submatrix of coefficients of endogenous variables and rectangular submatrices of coefficients of exogenous variables:

In (4) \(X_{{ei}}\) and \(X_{{xi}}\) are vectors, respectively, of endogenous and exogenous activity levels and \(A_{{ee,i}} ,~A_{{ex,i,~~~}} A_{{xe,i,~~~}} A_{{xx,i}}\) corresponding submatrices from partitioning of \(A_{i} .~~\) Developing the expression, we can re-write (2) and (3) as follows:

This expression identifies one part of the system ( \(A_{{ex,i}} X_{{xi}} )~\) as a vector of exogenous demand levels and one part (( \(I - A_{{ee,i}} )X_{{ei}} )\) as a corresponding vector of endogenous supply levels necessary to satisfy the direct and indirect demand generated by the exogenous demand levels.

Subtracting the endogenous vector without the project from the one with the project, we obtain:

More synthetically, expression ( 6 ) can be re-written in difference notations as:

Solving for the endogenous variables, we obtain:

Expression ( 8 ) indicates that the variation of the endogenous variables of the model may be the consequence of three different shocks, all filtered through the matrix of multipliers of the endogenous sectors: (i) the autonomous variation of the exogenous variables (capital formation, exports or a specific vector of project expenditures); (ii) the variation of the SAM coefficient submatrix of the transactions within the endogenous accounts, and (iii) the variation of the SAM submatrix of the transactions between the endogenous and the exogenous accounts. Intuitively, the exogenous activities increases aggregate demand through the value chains quantified in the SAM, but may also introduce technological change, and induce a new pattern of transactions, boost or reduce existing connections and create new ones.

If one of the exogenous accounts is a specific investment project, in particular, consider the exogenous variation measured by the project cash flow over a time horizon \(t = 0,1, \ldots ..T\) and the changes in the SAM coefficients due to the changes of the project cash flow every year.

Indicating with the t subscript the time, the term \(A_{{ex,c,t}} \Delta X_{{x,t}} = A_{{ex,c,t}} \left( {X_{{xc,t}} - X_{{xs,t}} } \right)\) is the increase in project expenditure in the t th year, while ( \(\Delta A_{{ex}}^{t} )X_{{xs}}^{t}\) , is the change induced by the project into the counterfactual SAM matrix of the same period without the project. With no competing alternative ( \(X_{{xs,t}} = 0)\) , we have: \(~A_{{ex,c,t}} \Delta X_{{x,t}} = A_{{ex,c,t}} X_{{xc,t}}\) , i.e., the project cash flow. This includes, as all columns of the SAM, the demand increases (with respect to the situation without the project) generated by the expenditures of the project with respect to all sectors. These expenditures include both costs and net benefits of the project such as the payments made to project stakeholders as for example the net margins paid to capital and the other net benefits, accounted in gross terms in a corresponding row of the SAM. The term \(\left( {\Delta A_{{ext}} } \right)X_{{xt}} = (A_{{ext + 1}} - A_{{ext}} )X_{{xs}}^{t}\) represents the structural impact of the technology embodied in the project. This impact may be due to different project requirements in terms of use of intermediate inputs and value added in comparison to existing technologies. Project impact is thus evaluated as the sum of two components, one depending on the change in the scale of the cash flow, and one depending on the change of the weights of the different items of the project transaction vector in a new SAM updated to account for the transactions introduced by each phase of the project.

The present value at rate of discount r of project impact can be directly derived from Eq. ( 8 ):

However, \(A_{{ee,t + 1}}\) will approximately remain constant if the project is small enough, and \(\Delta A_{{eet}} \cong 0\) , so that expression ( 9 ) can be approximated on the basis of the initial SAM for the endogenous accounts:

Expression ( 10 ) allows to estimate the present value of project impact using a single SAM and its variations as the direct and indirect effects of the present values of the project cash flows. In turn these are defined as the sum of two components: (i) the yearly project outlays for a given structure of the interdependencies between the project and the rest of the economy, and (ii) the yearly increases in the same outlays due to the variation of these interdependencies brought about by the changes of project outlays over time.

3.1 Building a project SAM

In the theory of cost–benefit analysis, actual transactions in the form of revenues and expenditures at market prices are associated with the so-called “financial analysis”, which has the purpose to evaluate projects from the point of view of a private subject. In the “economic analysis”, instead, the basis to compute benefits and costs are no longer actual transactions at market prices, but virtual transactions that reflect what consumers or producers gain from market exchanges and other project effects, but not necessarily pay for them. Two typical concepts used to quantify these values are the well-known constructs of consumer and producer surplus.

While definitions can be made more precise by a better specified theoretical context, both consumers and producers surplus have a long history in economics as their definition and initial use is due to Marshall ( 1890 ), one of the founding fathers of microeconomic theory. Consumer surplus can be defined as the excess of willingness to pay for a good or service, compared to what consumers actually pay, while producer surplus can be similarly defined as the excess of the price received compared to producers’ willingness to accept. In both cases, therefore, a measure of the difference between a virtual transaction and an actual one is used as a monetary measure of a real gain. While an increase in household income is matched by an increase in consumer expenditure and/or savings, an increase in consumer surplus does not apparently result in an increase in transactions. However, as shown by Weitzman ( 1988 ), real income, calculated with an appropriate price deflator (a Laspeyres index in case of homothetic preferences), is essentially equivalent to consumer surplus. This implies that any increase in consumer surplus is equivalent to the sum of the increase in income at baseline prices plus a term (of a second order of magnitude) reflecting the fact that real income is also larger because of as concomitant favorable change in relative prices. For example, suppose that the project determines an increase in market supply of a particular good and this determines a corresponding increase in consumer expenditure. This larger expenditure in turn can be decomposed in a consumption saving (i.e., a gain in income) for the quantity consumed without the project (i.e., for those who already consume the good) plus an increase in expenditure due to the fall in price.

In addition to consumer and producers surplus, cost–benefit analysis also takes into account a number of other benefits and costs that are not translated into market transactions, either because they are not the result of market exchanges or because market exchanges do not reflect “true” social values. In other words, these two classes of project effects reflect the so-called “shadow prices”, which differ from market prices of an amount reflecting the components of social values that for various reasons are not internalized by existing markets. Since the important work of Ronald Coase ( 1937 ), these externalities have been recognized as themselves corresponding to virtual (or potential) transactions.

Table 1 shows how benefits and costs of a project can be incorporated in the project row (project receipts) and columns (project outlays). The economic components of project receipts are: (i) revenues from project intermediates; (ii) revenues from consumer purchases; (iii) government subsidies; (iv) debt or equity financing, and (v) exports. The corresponding project costs are: (i) capital and operational costs; (ii) net margins (credited to capital); (iii) consumer incomes, credited to households; (iv) taxes; (v) interests and dividends, and (vi) imports. The project row and column at market prices balance since net margins, i.e., the difference between revenues and costs are included in the project SAM column as a cost for capital. In this way the two sums, respectively, of the column and the row entries for market transactions (the so-called “financial analysis” of the project) equal the project gross revenues, i.e., all receipts. In order to go from financial to economic analysis all the above variables must valued at shadow prices and conform, as indicated in Weitzman ( 1976 ), Eisner ( 1988 ) and Hartwick ( 1990 ) to the notion of Net National Product (NNP), i.e., to an ideal flow measurement of national wealth of a dynamic economy. They can thus also incorporate social welfare effects, externalities and natural capital (last three rows and columns in the matrix) for which market prices are not available (as in Banerjee et al. 2016 ).

3.2 Economic analysis

Tables 2 and 3 show an example of a social accounting matrix incorporating a project cash flow, respectively, in the construction period ( t  = 0), and in the operational period ( t  = 1), with the project cash flow being accounted for as an extra activity and/or institution in the SAM. The cash flow data in the construction period include only capital expenditure (capital goods produced by activities) in the account column and financing from capital formation in the account row. In the operational period, the project account column includes all project costs (capital replacement and operational costs), while the row account contains all project receipts. The value added account is credited in the project column of the payments to labor and capital, including the returns paid as net business margins to project sponsors. These payments amount to the sum of the inflows reported in the row minus all the costs (other than value added) reported in the column. As a consequence, the sum of the column and the sum of the row both amount to the gross revenue component of the project cash flow. In a more detailed account, with value added split between various types of production factors and a capital account, wages would be paid to different types of labor and the net margins from the project would be explicitly credited to capital. On the other hand, while net benefits depend on the return to capital, they also include the indirect effects on the economy which are credited to households, government or other accounts.

As Table 2 shows, in the construction period, the project is assumed to produce no revenues, while its costs are assumed to be 100 monetary units, with payments to activities, production factors (value added), and rest of the world (ROW). These costs are entirely financed from capital formation and give rise, to the extent that they mobilize unemployed resources, to value added increases through indirect effects. Revenues, on the other hand, are collected by the project as listed in the project row in Table 3 . These revenues are collected from various stakeholders who purchase the goods or services provided by the project, including households and government. With no indirect effects, project net (financial) benefits would thus simply be the portion of value added credited to capital net of any charges due to user costs for maintenance.

Project financing is then repaid in the operational period with interests (120 monetary units versus the 100 units borrowed for construction). In this period (Table 3 ), the project is assumed to collect revenues equal to 365 units from all sectors and institutions, with intermediate costs of 20 units from domestic activities and 25 units from imports. The difference between project receipts of 365 and project intermediate costs of 45 is credited for 120 units to the capital formation account and for 200 units to the value added account and add to total project outlays, including loan repayment with interests accrued to capital formation. As a consequence, the value added account in the operation period is the sum of the project direct payments to production factors and indirect taxes to meet operational costs and of the returns to capital obtained from project revenues after paying for intermediate goods and capital formation. The capital formation expenditures include loan repayments, interests, capital depreciation (assumed to be 5% per year) and any expenditure for replacement of capital goods.

The two transaction matrices in Tables 2 and 3 correspond to two coefficient matrices, whose difference is reported in Table 4 .

Assuming as exogenous accounts, in addition to the project, capital formation and the rest of the world, we can now compute the project impact in both periods according to expressions ( 8 ) and ( 9 ).

Table 5 reports the values of the main SAM accounts affected by the project, while Table 6 compares outcome variables with project costs. Multiplier estimates from value added and, considering depreciation charges, for Net National Product (NNP) are around 1 for the construction period and around 1.5 for the operational period, where not only costs but also net revenues from the project are taken into account.

4 Discussion

In this paper, I have presented a methodology to integrate cost–benefit analysis in the impact evaluation performed on the basis of social accounting principles (SAM or SAM-based models). The integration requires simply a recasting of the economic and/or financial data used in the discounted cash flow analysis in the format used in the SAM accounts and involves a simple reclassification of costs and revenues according to the statistical system used in the SAM (Eisner 1988 ). The methodology generalizes the use of multipliers to evaluate the short-term impact of investment projects, which is typically used alongside cost–benefit analysis, but without a clear relation with both its theoretical principles and practical applications. Unlike the simple multiplier method, by integrating project accounting in the SAM, it allows an exhaustive analysis of impact on revenues, costs and financing, thus providing a clear picture of the project contribution to both demand and supply both in the project construction and operational periods. By integrating in a consistent accounting framework value added formation and economic benefits and costs, this method also allows making full use of the information provided by the sector and the distributional details of investment cash flows through the entire project life.

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Scandizzo, P.L. Impact and cost–benefit analysis: a unifying approach. Economic Structures 10 , 10 (2021). https://doi.org/10.1186/s40008-021-00240-w

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Cost-Benefit Analysis and the Evaluation of the Effects of Corruption on Public Projects

Chapter 15 in Handbook of Research on Cost-benefit Analysis, (ed.) Robert J. Brent, Edward Elgar Publishing, 2009; ISBN: 9781847200693

26 Pages Posted: 22 Sep 2018

Robert J Brent

Fordham University

Date Written: July 22, 2009

Cost–benefit analysis (CBA) is the basis for rational economic decision making, whether it is for the government or individuals. If benefits are greater than costs, then a project or activity should be expanded. If costs are greater than the benefits, the project or activity should be contracted. And if benefits equal costs, the existing scale of operations is optimal. A social CBA obtains its measurement principles concerning the benefits and costs from applied welfare economics. Its main purpose is to incorporate considerations into public expenditure decision making that caused private market decision making to fail to produce optimally (due to the existence of externalities) or produce at all (pure public goods). However, these corrections for market failure lead only to potential gains. Once the public sector is involved there is also the potential for government failure. Public officials may have an agenda that is different from social welfare maximization. In particular, if these public officials are corrupt and try to maximize bribes which they keep for themselves, then this reality needs to be included as part of the cost–benefit evaluation. The Executive Board of the World Bank defined corruption in 1997 as: ‘The use of public office for private gain’. This definition is also the one used by Bardhan (1997) in his survey of corruption and development.

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Benefit-cost analysis ; CBA

CBA is a framework employed to assess both the social and private costs and benefits of a project (program, scheme, intervention, or policy) with the aim to determine whether the project is desirable from the social welfare perspective. In order to assess the welfare change attributable to the project (i.e., the project’s net benefit to society as a whole), all costs and benefits resulting from the project must be quantified and transformed into monetary values . CBA is used in ex ante evaluation as a tool for policy makers to select alternative projects or to decide whether a specific scheme is worthwhile for society. It can be also employed ex post to quantify the net social worth of a fully implemented specific program.

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CBA is a normative procedure involving making value judgments for policy interventions. Accordingly, CBA has its roots in welfare economics , a branch of economics that deals with ethical propositions in order...

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Departamento de Economía Aplicada (Estructura Económica), Universidad de Málaga, Málaga, Spain

Bienvenido Ortega

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Correspondence to Bienvenido Ortega .

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Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Roma, Italy

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Directorate for methodology and statistical process design, Italian National Institute of Statistics, Rome, Italy

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Ortega, B. (2021). Cost-Benefit Analysis. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-319-69909-7_600-2

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Cost benefit analysis is a systematic process for calculating and comparing benefits and costs of a project. A cost benefit analysis finds, quantifies, and adds all the positive factors (the benefits). Then it identifies, quantifies, and subtracts all the negatives (the costs). The difference between the two indicates whether the planned action is advisable. The real trick to doing a cost benefit analysis well is making sure you include all the costs and all the benefits and properly quantify them.

• An Introduction to Cost Benefit Analysis San José State University Department of Economics
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Benefit–Cost Analysis of Undergraduate Education Programs: An Example Analysis of the Freshman Research Initiative

• Rebecca L. Walcott
• Phaedra S. Corso
• Stacia E. Rodenbusch
• Erin L. Dolan

Department of Health Policy and Management, University of Georgia, Athens, GA 30602

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Texas Institute for Discovery Education in Science, College of Natural Sciences, University of Texas at Austin, Austin, TX 78712

*Address correspondence to: Erin L. Dolan ( E-mail Address: [email protected] ).

Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602

Institutions and administrators regularly have to make difficult choices about how best to invest resources to serve students. Yet economic evaluation, or the systematic analysis of the relationship between costs and outcomes of a program or policy, is relatively uncommon in higher education. This type of evaluation can be an important tool for decision makers considering questions of resource allocation. Our purpose with this essay is to describe methods for conducting one type of economic evaluation, a benefit–cost analysis (BCA), using an example of an existing undergraduate education program, the Freshman Research Initiative (FRI) at the University of Texas Austin. Our aim is twofold: to demonstrate how to apply BCA methodologies to evaluate an education program and to conduct an economic evaluation of FRI in particular. We explain the steps of BCA, including assessment of costs and benefits, estimation of the benefit–cost ratio, and analysis of uncertainty. We conclude that the university’s investment in FRI generates a positive return for students in the form of increased future earning potential.

INTRODUCTION

According to the National Center for Education Statistics, postsecondary institutions in the United States spend ∼$150 billion annually on instruction ( National Center for Education Statistics, 2016 ). Grants from federal, state, and philanthropic agencies provide additional funds for the development, testing, and evaluation of innovative undergraduate education programs, which, if demonstrated to be effective, often are expected to be sustained from other sources when grant funding ends. Yet the changing landscape in postsecondary education, including increasing enrollment, expanding access, and decreasing state-level investment, is putting added pressure on postsecondary education budgets ( Pew Charitable Trusts, 2015 ). How can administrators make informed decisions about how to invest limited funds? How can the directors of undergraduate education programs determine whether their initiatives yield sufficient benefits to be worth the cost, and how can they provide such evidence to administrators? Of course, many factors must be considered when making decisions about how to invest funds, including alignment of particular initiatives with institutional missions, priorities, and strategic plans. This Research Methods essay aims to add an additional tool to the decision-making toolbox: benefit–cost analysis.

Benefit–cost analysis (BCA) is one method of economic evaluation, or the systematic analysis of the relationship between costs and outcomes for a given program or policy. The purpose of economic evaluation is to provide stakeholders with information for making decisions about how to allocate resources, such as whether the benefits of the program outweigh its costs and whether returns on investments are sufficient to justify continued or even expanded funding for a program. The National Academies of Sciences, Engineering, and Medicine recently released a consensus study designed to improve the use of economic evidence to inform investments in children, youth, and families ( Steuerle et al. , 2016 ). Here, we offer a general guide for researchers and practitioners looking to conduct BCA to yield such evidence.

Two main types of economic evaluation are cost-effectiveness analysis (CEA) and BCA. CEA compares the costs of a program with its impacts measured in natural units, or units that occur in real life, such as college attrition rates. For example, CEA might yield information about the percentage by which college attrition rates are reduced per dollar spent on a program aimed at retaining students in college (i.e., $X spent results in Y% reduction in attrition). BCA compares program costs with program outcomes or impacts that have been monetized, or expressed in dollars. For instance, BCA could provide information about the extent to which a program increases a college student’s future earning potential for every dollar spent on the program (i.e., $X spent results in $Y increase in future earning potential). BCA produces several summary measures, including the ratio of benefits to costs (i.e., benefit–cost ratio [BCR]) and benefits minus costs, or net benefits, both of which are presented in net present dollars; that is, dollars expressed at a current value as opposed to a past or future value. Return on investment (ROI) analysis is a subset of BCA, in which results are presented as the percentage of the program cost that is returned as a program net benefit. For example, ROI might provide information about the benefit of an educational institution’s recruitment campaign in terms of increased tuition income for the institution ($X spent on recruitment yields $3X in tuition income, or an ROI of 200%). Table 1 presents the key features of CEA, BCA, and ROI for a hypothetical disease vaccination program. The hypothetical program costs $5000 and averts 50 cases of the disease. The cost of disease treatment is assumed to be $250. Therefore, the benefits of the program are $12,500 (50 cases averted * $250 per case) and the net benefits are $7500 ($12,500 − $5000).

Although economic evaluation is commonly used to evaluate healthcare and public health interventions ( Haddix et al ., 2003 ; Drummond et al ., 2015 ; Neumann et al ., 2016 ), regular application of economic evaluation in the field of education has primarily focused on early childhood educational programs ( Barnett, 1985 ; Lee et al ., 2012 ; Karoly, 2016 ). The application of economic evaluation to postsecondary education programs and policies is more nascent, as evaluations in these contexts face increased study design challenges as well as a lack of standardized outcome measures ( Hummel-Rossi and Ashdown, 2002 ). Our purpose with this essay is to describe methods for conducting a BCA as one approach to economic evaluation, using an example of an existing undergraduate research experience (URE) program, the Freshman Research Initiative (FRI) at the University of Texas Austin (UT Austin). We hope this example will be useful for demonstrating how to apply economic methodologies to evaluate an undergraduate education program and also for evaluating the costs and benefits of FRI in particular.

FRI was developed at UT Austin to engage students in multiple semesters of course-based undergraduate research experience (CURE) early in their college careers, with the goal of increasing students’ persistence in scientific degree programs and careers. FRI makes use of an expanded apprenticeship model, integrating large numbers of undergraduate students into research groups, called “research streams,” as an alternative to entry-level laboratory courses. The program comprises a three-course sequence taken within the first 2 years of undergraduate study. In each stream, groups of 35–40 undergraduate students work on a common research problem with mentorship and guidance from a PhD-trained research educator (RE) and a tenure-track/tenured principal investigator. The RE role is unique and essential to FRI, because each RE mentors 35+ students in his or her stream, which would not be practical in a more traditional research group structure. REs are immersed in the cognitive apprenticeship model of interaction with the students ( Ritchie and Rigano, 1996 ), creating and implementing a research program designed to support students in learning core science concepts and research skills while making meaningful scientific contributions (e.g., authorships on peer-reviewed publications). FRI capitalizes on the power of research experiences as a science, technology, engineering, and mathematics (STEM) recruitment and retention tool, integrating a combination of experiences that contribute to student success: mentoring ( Coppola, 2001 ), tutoring ( Topping, 1996 ), research experiences ( Lopatto, 2007 ; Russell et al ., 2007 ; Hurtado et al ., 2009 ), and learning communities ( Springer et al ., 1999 ).

A recent consensus study from the National Academy of Sciences highlights the need for more research evaluating the benefits and costs of UREs, particularly for students majoring in STEM fields ( National Academies of Sciences, Engineering, and Medicine, 2017 ). Several existing studies discuss the benefits and costs of UREs but do not attach a monetary value to program benefits and costs, which precludes economic evaluation ( Pennebaker, 1991 ; Hoffman, 2009 ; Lei and Chuang, 2009 ). These studies are undoubtedly helpful in providing information about the supports and constraints of implementing a program. Yet there is a clear gap in knowledge regarding the potential returns of allocating resources to UREs.

Here we focus on BCA, the first step of which is to clearly define the program of interest and to identify the program’s alternative, or the “status quo” experience ( Karoly, 2016 ; Steuerle et al. , 2016 ). The status quo experience refers to the program or intervention that the study population would receive if they were not participating in the program being evaluated. In many cases, the status quo refers to a “do-nothing” approach. This alternative experience serves as the baseline for comparison to most accurately capture the costs and benefits attributable to the program. For our example, FRI is the program of interest and STEM majors who do not participate in FRI take comparable non-CURE science courses (i.e., the status quo experience), referred to hereafter as the comparison program.

After the program and the alternative have been clearly defined, it is necessary to have demonstrated evidence of program effectiveness, or the impact of the program compared with the alternative. An important point to remember is that the usefulness of an economic evaluation rests on the robustness of the underlying effectiveness study. The effectiveness study should be conducted with a comparable population, should incorporate valid methodology, and should produce outcomes amenable to economic analysis. FRI is a good candidate for BCA, because it is an example of a large-scale URE program for which effectiveness has been demonstrated ( Rodenbusch et al ., 2016 ). Participation in FRI has been shown to increase overall graduation rates from 66% for comparison students to 83% for FRI students, after carefully controlling for other factors that affect graduation rates using propensity score matching. FRI has also been shown to increase the percentage of students graduating with a STEM major (instead of transferring to a non-STEM major) from 71% for comparison students to 94% for FRI students. See work from Beckham, Rodenbusch, and colleagues for more details about FRI and its effects ( Beckham et al ., 2015 ; Rodenbusch et al. , 2016 ). These results, along with data on the costs of FRI and the comparison experience, allow for a BCA of the program to be conducted, which we describe in the following section.

To conduct a BCA of FRI, we estimated all costs incurred by UT Austin for both FRI and the comparison program. We conceptualized program benefits as the estimated future potential earnings of FRI students relative to comparison program students. We used a BCR as the summary measure, estimating the ratio of benefits to costs for FRI in relation to the comparison program. We illustrate this process in Figure 1 and describe each step in the following sections.

FIGURE 1. Flowchart for BCA outlining the steps necessary to conduct and interpret a BCA for a program.

Estimating Costs

We started by conducting a programmatic cost analysis, which is the standard first step in economic evaluation including BCA and refers to the collection, valuation, and analysis of all resources required to implement a program or policy. We first determined the perspective of the analysis (i.e., who bears the costs), as the perspective will drive which cost data we choose to collect. For example, if we analyze the program from an organizational perspective—in the case of FRI this would be UT Austin—we would only collect data on costs paid by the organization. Alternatively, we could frame the analysis from a societal perspective and include costs to participants as well as the organization, such as time and travel costs. Best practices for economic evaluation indicate using the societal perspective in order to provide the most comprehensive picture of benefits and costs ( Steuerle et al. , 2016 ). However, recommendations for education studies emphasize the importance of matching the study perspective to the goals of the evaluation, especially in cases when a societal perspective would unnecessarily complicate the interpretation of the study findings ( Barnett, 1993 ; Hummel-Rossi and Ashdown, 2002 ). Here, we analyzed costs from the university’s perspective and did not consider the costs to students to participate in FRI, because we did not anticipate that costs to FRI versus comparison students would differ (i.e., students do not pay extra costs to participate in the program). We also did not include an estimate of any potential cost differences in terms of the time or effort students spent in FRI versus the comparison experience. We could not reliably estimate these values and did not expect them to differ appreciably between the two experiences, although this should be tested more directly in future studies.

Once an analytic perspective is selected, costs are collected prospectively or retrospectively , using a microcosting or gross-estimation approach. Prospective cost collection refers to the ongoing recording of program costs as they accrue, such as through activity logs, project invoices, and travel logs. Retrospective cost collection involves estimating expenditures after program implementation. Microcosting involves collecting costs by identifying individual resources, while gross estimation uses total program expenditures as costs (e.g., from budgets). Prospective microcosting for both preimplementation (i.e., planning and development) and implementation phases is preferred, because it provides the most detail about the resources required to implement a program and therefore is the most useful for program implementers ( Steuerle et al. , 2016 ). However, this method requires the most evaluator effort and is not always feasible.

For our BCA of FRI, we used a retrospective microcosting approach from the university perspective. We obtained itemized expenditure data from the FRI program, including the individual personnel costs (salary plus fringe for instructors/REs and graduate/undergraduate assistants) and materials costs for each FRI course and for each comparison course. Regarding indirect costs, the FRI program was found to use more building resources than the comparison experience due to students spending more time in campus labs for assignments: FRI courses 1 and 2 used ∼75% more building resources than their comparators, and FRI course 3 was set equal to FRI course 2, while comparison course 3, an independent study, used no building resources. Indirect (or overhead) costs can be allocated several different ways depending on the information available, but it is generally recommended that allocation be tied to resource use, such as total direct costs or total personnel costs ( Drummond et al. , 2015 ; Steuerle et al. , 2016 ). We estimated building resource costs as a proportion of total personnel costs based on the allocation found in the University of Texas system’s annual financial report. In other words, we set building resource costs at 2.8% of total personnel costs for comparison courses 1 and 2 ( University of Texas System, 2016 ). There was no marginal difference in administrative costs and institutional support costs between the FRI program and the comparison experience, as both course sequences require similar levels of administration and coordination. Therefore, these costs were not included.

We focused our cost collection on implementation costs, because planning and development costs were not available and may also differ significantly between institutions planning FRI-like programs. We provide a summary of the costs of FRI and the comparison experience in Table 2 . Because different courses had different levels of enrollment, we present costs at the per student level. On average, FRI costs $2875 per student, while the comparison program costs $1820 per student.

a Owing to rounding, there may be slight discrepancies in sums.

Estimating Benefits

Before a BCA can be conducted, the benefits of the education program of interest must be identified and monetized. Benefits can be multiple and far-reaching, accruing to students in the form of higher grade point averages and to institutions in the form of reputation. Rodenbusch and colleagues (2016) identified benefits of FRI by comparing outcomes of students who participated in FRI versus a propensity score–matched group of students who participated in the comparison experience. They found that FRI participation led to significant increases in likelihood of graduating from college (from 66 to 83%) and significant improvements in rates of STEM retention (from 71 to 94%) ( Rodenbusch et al. , 2016 ). STEM retention refers to the sample of students who entered college with a declared STEM major and graduated from college with a STEM major. Because FRI had demonstrated effectiveness for these two outcomes, we used college graduation and STEM major to define our study benefits. Attrition from college (i.e., not graduating) represents a third, complementary outcome for our study population.

The outcome must be monetized to turn a study outcome into a benefit for BCA. Future earning potential is a common outcome measure for BCAs in education ( Stem et al ., 1989 ; Hummel-Rossi and Ashdown, 2002 ; Karoly, 2016 ), and we chose to use future earning potential as a monetization for each of the three outcomes. Data provided by the Hamilton Project at the Brookings Institution show career earnings by educational attainment and by college major, both as median annual earnings over a career and as median lifetime earnings ( Hershbein and Kearney, 2014 ). The earning potentials generated by the Hamilton Project are comparable to other estimates when adjusted for discounting and inflation ( Carnevale et al ., 2013 ). By categorizing the majors into two groups, STEM and non-STEM, we were able to estimate a median potential earnings value for each group. We defined our study benefits in terms of median initial annual earning potential and median lifetime earning potential for each of our three study outcomes: college attrition, graduation with a STEM degree, or graduation with a non-STEM degree. College graduates have a much higher earning potential than those who leave college without graduating, and STEM graduates have a higher earning potential than graduates with majors in non-STEM fields. We summarize the benefits for this analysis in Table 3 .

a Lifetime earnings are discounted annually at a rate of 3%.

It should be noted that, instead of directly comparing institutional costs to institutional benefits, we are chose to measure benefits exclusively from the student perspective. Many additional benefits of FRI could also be monetized and analyzed, such as the tuition dollars gained and reduced recruitment costs from increased student retention, as well as benefits to the reputation of UT Austin ( Heldman, 2008 ; Raisman, 2013 ). Increased retention is also likely to save state and federal governments money in the form of publicly funded scholarships and grants provided to students who end up dropping out ( Schneider, 2010 ). Finally, from a societal perspective, increased graduation rates and increased STEM graduation rates in particular are likely to produce societal benefits in the form of technological progress and increased economic productivity ( Krueger and Lindahl, 2001 ). Including additional benefits in our analysis may have resulted in a more comprehensive BCA, but the challenges inherent in monetizing more abstract and distal benefits, such as quantifying reduced recruitment costs in terms of FRI effectiveness and how to value increased university reputation, would likely have weakened the overall usefulness of the study. We opted not to monetize these additional benefits in order to maintain a simplified focus on the organizational and student perspectives and in an effort to prioritize the explanation of the BCA process for this essay.

For making fair cost comparisons, it is important to ensure costs and benefits are adjusted for inflation and for time preference (i.e., discounting), especially for cases in which the benefits occur in the future. The value of a dollar 5 years ago does not equal the value of a dollar today, and adjusting for inflation mitigates this difference in purchasing power. For this study, we collected FRI costs for 1 year of operation in 2015 and obtained benefits data presented in 2014 U.S. dollars. To adjust for inflation, we used the All Items Index of the Consumer Price Index to adjust all costs to the base year of 2014, so that all dollar values of costs and benefits possess equal purchasing power ( Bureau of Labor Statistics, 2014 ). Discounting is separate from inflation and can be defined as the reduced valuation of costs and benefits that occur in the future due to the concept of time preference. Time preference refers to the advantage of obtaining a benefit now instead of in the future, and this preference holds true even in a scenario in which inflation does not exist. The costs of FRI do not need to be discounted, as they occur in a single year; however, the benefits of FRI accumulate over the course of the student’s career, and this differential timing of costs and benefits necessitates discounting. Therefore, the future earning benefits obtained from the Hamilton Project were discounted at a 3% annual rate (a common discount rate for social programs) and are reported as present values in order to be fairly compared with the program costs ( Hershbein and Kearney, 2014 ). Additional adjustments for monetized benefits may be necessary to ensure that the transfer of benefits from one source accurately applies to the population under consideration. For example, a geographic cost of living adjustment may be required if benefits estimates derived from a Los Angeles population are applied to participants of a program implemented in the Midwest. Because the future earning potential estimates used in this study were derived from a nationally representative sample and we have no reason to believe that UT graduates significantly over- or underearn when compared with the national average, no further adjustments are necessary.

Modeling Costs and Benefits

The decision tree is among the most common methods for modeling economic evaluations ( Drummond et al. , 2015 ). A decision tree ( Figure 2 ) functions like a flowchart, with a hypothetical population beginning at a decision node (rectangle) and then proceeding through the tree sequentially until arriving at a final outcome represented by a terminal node (triangle). Along the route are chance nodes (circles) at each bifurcation in the tree, which represent the probability of a given event occurring along the pathway. At the end of the tree, each terminal node represents a final outcome associated with the pathway of events. The probability of each final outcome occurring can be calculated by multiplying the probabilities at each chance node along a particular path through the tree. The average projected cost and benefit of each path can then be calculated and compared.

FIGURE 2. Decision tree model for the costs and benefits of FRI vs. the comparison program. The potential population of FRI is STEM majors at a decision node (rectangle on left), who either become part of the FRI or comparison group. Chance nodes (circles) are points where the population has different likelihoods of pursuing different paths on the way to realizing different outcomes (triangles). The percentages of the population that proceed on each path are noted next to the path. The probability of each outcome occurring is calculated by multiplying the probabilities at each chance node (i.e., the percentages) associated with that path. The average cost and benefit of each path can then be calculated and compared (on right).

Figure 2 depicts a decision tree for estimating the projected costs and benefits of FRI versus the comparison program. The hypothetical population consists of STEM majors who participate or not in FRI (the decision node). For simplicity’s sake, we assume a population of 200 STEM majors, with 100 in the FRI path and 100 in the comparison path. Following the two populations sequentially through the tree, the evidence from Rodenbusch et al. (2016) suggests that one would expect 17% of the FRI students to leave college and 34% of comparison students to leave college. Of the 83 FRI students who graduate college, 94% (i.e., 78 students) go on to graduate with a STEM degree, while the remaining five graduates do not. Similarly, 71% of the 66 comparison students who graduate (i.e., 47 students) do so with a STEM degree, while the remaining 19 comparison students graduate in a non-STEM field.

The right side of the decision tree ( Figure 2 ) shows the expected probability for each group (FRI vs. comparison) of achieving each of the three outcomes and the associated potential earnings. Probabilities are rounded to the nearest hundredth in this example. Because average costs do not vary among outcomes within each study group, they are shown only at the decision node. Average potential earnings for each study group are estimated with the expected outcome probabilities as weights. On the basis of this model, we estimate that the expected average median initial annual earning potential for FRI students is $27,658, while the expected average median initial annual earning potential for comparison program students is $23,305. The expected average median lifetime earning potential for FRI students is $1,284,400, while the average median lifetime earning potential for comparison students is $1,106,450.

Benefit–Cost Analysis

The next step in a BCA is to directly compare the program’s costs with its benefits. It is common for BCAs to compare a program with a scenario in which there is no program in place, referred to as a “do-nothing” scenario. A do-nothing scenario has a cost of $0, making the calculation of the BCR relatively straightforward: the benefits of the new program are compared with the costs of the new program. For example, BCAs of early childhood education programs often compare a preschool population with a population that did not attend preschool. The benefits are estimated from the improved performance of the preschool graduates compared with children who did not attend preschool, and the BCR reflects these benefits compared with total program costs. However, when the comparison experience is not a do-nothing scenario but instead refers to a basic program or the status quo, which the program of interest is enhancing, it is more appropriate to compare the incremental costs and benefits in a BCR ( Karoly, 2016 ).

In this study, we compare FRI with a traditional college course sequence, the status quo in this case, instead of a do-nothing scenario; therefore, we estimated the incremental, or additional, costs and benefits of FRI relative to the costs and benefits of the status quo. The microcosting data indicate that FRI costs an average of $1055 more per student than the comparison program. The projected benefits indicate that participation in FRI produces an average increase of $4353 per student in potential initial annual earnings and $177,950 per student in potential lifetime earnings. Thus, FRI participants are estimated to earn almost 19% more in initial annual earnings upon graduation and 16% more in lifetime earnings when compared with the comparison group. Calculating an incremental ratio of benefits to costs reveals a 4.13:1 ratio for initial annual earning potential and a 169:1 ratio for lifetime earning potential. Any ratio greater than 1:1 indicates a positive return on the university’s investment. Thus, we estimate that FRI generates a return of more than $4 in students’ initial earning potential and a return of $169 in students’ lifetime earning potential for every $1 that the university invests when compared with the earning potential of students in the comparison program.

The final step of an economic evaluation, including BCA, is to conduct a sensitivity analysis ( Drummond et al. , 2015 ; Steuerle et al. , 2016 ). Costs and benefits of a program may vary among participants, and a sensitivity analysis is conducted to reflect this uncertainty. For this study, we conducted a two-way sensitivity analysis, in which we varied two key parameters both individually and simultaneously in order to present potential scenarios producing lower- and upper-bound estimates to supplement our baseline estimate of the BCR.

The first key parameter we varied for the sensitivity analysis was the average cost per student of the FRI course sequence, as this cost depends on the resource intensity of the research undertaken in each course. For example, computer science courses required far fewer resources than wet-lab science courses. Our data showed that total FRI program costs ranged from $2137 to $3785 per student for the three courses; therefore, we assumed FRI costs of $2137 per student in a low-cost scenario and a cost of $3785 per student in a high-cost scenario. A more robust sensitivity analysis would also include the associated differences in effectiveness by course type, but these data were not available in the effectiveness study.

The second key parameter we varied was the graduation rate of students in the hypothetical cohort. Our baseline scenario assumed that students who left UT Austin did not finish college elsewhere and accrued the future earning potential of students who never finish college. For the sensitivity analysis, we add a scenario in which 10% of students who leave UT Austin enroll in and graduate from a different college ( Schneider, 2010 ) and thus accrue the future earning potential of a non-STEM graduate.

Table 4 presents the results of a two-way sensitivity analysis. The estimates of FRI costs are given in the first column, followed by the BCRs for the baseline graduation assumptions and then the ratios for the increased graduation rates. The lowest BCR scenario (the costliest FRI course sequence and the increased graduation rate) reduced the incremental BCR to 2.12:1 for initial annual earnings and 88:1 for lifetime earnings, while the highest BCR scenario (the least costly FRI courses and the baseline graduation rate) increased the BCR to 13.7:1 for initial annual earnings and 561:1 for lifetime earnings. Therefore, every additional dollar that UT Austin invests in FRI when compared with a traditional program of study generates $2 to $14 in returns for students in increased potential initial annual earnings and $88 to $561 in returns for students in increased potential lifetime earnings. In all scenarios, even the most costly FRI courses generate a positive return for students.

a Increased graduation rate assumes that 10% of those who leave college go on to graduate from a different institution and thus gain the earning potential of a college graduate.

LIMITATIONS

There are several limitations to our FRI analysis that should be considered in any economic evaluation. First, best practices for economic evaluation recommend a societal rather than organizational perspective in order to provide the most comprehensive economic estimates ( Steuerle et al. , 2016 ). Such a perspective would account for any marginal differences in student costs between the two programs, including marginal differences in time spent on course work. However, a comprehensive societal perspective requires more extensive data collection and does not always have a straightforward and applicable interpretation. In our example analysis of FRI, we made use of multiple perspectives in an effort to most clearly and succinctly illustrate how the university’s investment can benefit FRI students. Specifically, we estimated costs from an organizational perspective, as UT Austin (the organization) funds the extra programmatic costs of FRI (students do not pay extra fees to participate in the program). We estimated benefits as future earning potential from a student perspective, because improving student retention in college and in STEM were primary objectives of the program. Multiple perspectives are not uncommon in BCA; BCAs of government programs often incorporate multiple perspectives, as costs are typically estimated from the government’s perspective, while benefits accrue to vulnerable populations who may not contribute to the tax base for the program. Public education funding serves as a useful example, because property taxes fund a large proportion of public education, but not all who pay property taxes have children using public education and not all who benefit from public education pay property taxes. Such a multiperspective approach does, however, preclude a traditional ROI analysis, as the student benefits do not necessarily return to the investor (the university). An analysis that estimated benefits in terms of increased tuition dollars or attributable alumni donations would allow for an estimation of ROI.

Second, there are limitations in our assumptions of costs and benefits. We did not include preimplementation costs, such as planning costs, in our estimate of FRI costs. Including these costs would decrease the BCR, although over time this impact would lessen as these costs were spread over more FRI participants. Further, we obtained effectiveness data at the aggregate level only and therefore were unable to analyze costs and outcomes more precisely at the major level, which would have enabled BCR estimates by major. In our estimation of benefits, we did not have data on the actual earnings of FRI graduates and instead used existing national estimates to project the earning potential of STEM versus non-STEM graduates. We used median estimates instead of average earnings to mitigate the skewedness of the data, but variances in earning were unequal between STEM and non-STEM majors, with non-STEM majors realizing higher variance. Additionally, median earnings data were reported at the major level, and in order to report group-level earnings potential of STEM and non-STEM majors, we used the median earnings of the median major for each group. These are not the true median earnings of STEM and non-STEM majors, as those data were unavailable.

Finally, we presented a simple two-way sensitivity analysis, which introduced the concept of uncertainty, to encourage readers to consider how variability in assumptions may affect the evaluation’s conclusions. However, per guidance on how to conduct sensitivity analyses from the National Academies and others, a more robust sensitivity analysis should include varying all parameters in the model, uniquely and simultaneously ( Drummond et al. , 2015 ; Steuerle et al. , 2016 ). In our sensitivity analysis, there are no analyses of uncertainty around the benefit estimates of future earning potential or around the effectiveness of FRI, and a robust multiway analysis incorporating these parameters would be appropriate. The use of more sophisticated modeling techniques, such as probabilistic sensitivity analysis, would also strengthen the study.

A recent consensus study calls for research that evaluates the benefits and costs of UREs, particularly for students in STEM majors ( National Academies of Sciences, Engineering, and Medicine, 2017 ). Here, we aimed to provide more general guidance on how to conduct economic evaluations of undergraduate education programs by explaining the basic methods used to assess a program’s benefits and costs in a BCA. To illustrate how BCA can be used in practice, we conducted a BCA using a large-scale URE program, the FRI at UT Austin, the results of which can be used to inform decisions about the program. We conclude that the university’s investment in FRI is likely to generate a positive return for students in the form of increased future earning potential.

ACKNOWLEDGMENTS

We thank Lauren Crowe, Cassandra Delgado-Reyes, and Marty Mass for providing cost information. Support for FRI was provided by a grant from the Howard Hughes Medical Institute (#52006958). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of HHMI.

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Submitted: 11 July 2017 Revised: 8 December 2017 Accepted: 11 December 2017

© 2018 R. L. Walcott et al. CBE—Life Sciences Education © 2018 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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Infrastructure, education, environmental protection, and health care are examples of goods and services that in many circumstances are not produced by competitive private companies. Instead, decision making regarding investments and regulations is often made by politicians or public sector officials. For these decisions to be consistent, rational, and increase welfare, a systematic approach to evaluating policy proposals is necessary. Cost-benefit analysis is such a tool to guide decision making in evaluation of public projects and regulations. Cost-benefit analysis is a procedure where all the relevant consequences associated with a policy are converted into a monetary metric. In that sense, it can be thought of as a scale of balance, where the policy is said to increase welfare if the benefits outweigh the costs. Cost-benefit analysis of a proposed policy may be structured along the following lines:

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• Identify the relevant population of the project. For a cost-benefit analysis of a single individual or for a firm, this is not a problem. But in a societal cost-benefit analysis, we need to consider how to define society. A common approach is to consider the whole country as the relevant population. This is reasonable given that most public policies are financed at the national level. Another approach is to conduct a cost-benefit analysis specifying costs and benefits using different definitions of the relevant population—for example, including benefits and costs of a neighboring country in the analysis.
• Specify all the relevant benefits and costs associated with the policy. The aim with a cost-benefit analysis is to include all relevant costs and benefits with a policy. Based on the definition of the population (Step 1), every aspect that individuals in this population count as a benefit or a cost should be included in the analysis. For some benefits and costs of a policy, this may be an easy task—for example, 2,000 hours of labor input are required next year to build a road. But there are also more difficult phases in this step of a cost-benefit analysis; some examples include (a) a new infrastructure investment may have ecological consequences that are difficult to estimate and (b) regulating speed limits in a major city may have beneficial health effects due to decreases in small hazardous particles. For the economist or analyst, this step often consists of asking the right questions and gathering the necessary information from the literature, professionals, or both.
• Translate all the benefits and costs into a monetary metric. A cost-benefit analysis requires that the different consequences are expressed in an identical metric. For simplicity, we use a monetary metric ($ or €, etc.). Consider the example of an investment in a new road. The costs for the labor input can be valued by the wages (plus social fees, etc.), and the use of equipment may be estimated by the machine-hour cost. For benefits of, for example, increased road safety, decreased travel time, and decreased pollution level, there are no market prices to use; instead, weights and estimates are necessary to translate these benefits into a monetary metric. If this would not be possible with all relevant consequences, they should at least be included as qualitative terms in the evaluation. In the Valuation of Nonmarket Goods section, giving nonmarket goods a monetary estimate is discussed.
• If benefits and costs arise at different times, convert them into present value using an appropriate social discount rate. Most people prefer a benefit today to a benefit in one year. There are several reasons for this, one being that no one knows for sure whether he or she will be alive in a year. Another reason may be that, over a longer time horizon, people expect their incomes to increase in the future. If an extra dollar has a larger utility benefit to a less rich individual, that individual would prefer to consume when he or she has less money (today) rather than when he or she has more (e.g., in 5 years). Also, from an opportunity-cost approach, $100 that is not consumed today can be invested in a bond, and in a year from now, it may be worth $105, implying higher consumption in one year. In a cost-benefit analysis, economists therefore (generally) do not treat benefits and costs that occur at different times as equal; rather, they translate all benefits and costs into a present value. Choosing an appropriate social discount rate is, however, a complicated task and will often have major effects on the results of the evaluation. In the Discounting section, this is discussed in more detail.
• Compare the net present value of benefits and costs. When the present value of benefits (PVB) and costs (PVC) has been calculated, what remains is to calculate the net present value (NPV). The policy is said to increase social welfare if the net present value is positive—that is, PVB- PVC> 0. It is also common to express the comparison as the benefit-cost ratio—that is, PVB / PVC, which gives the relative return of the investment. If the ratio is greater than 1, the policy increases social welfare.
• Perform a sensitivity analysis to see how uncertain the benefit-cost calculation may be and give a policy recommendation. A cost-benefit analysis will have several uncertainties regarding the outcome. It is most often not reasonable to show only one point estimate of the evaluation. There are often uncertainties regarding both parameter values (such as the monetary estimates of, e.g., increased safety or environmental pollution) and more technical issues, such as the economic lifetime of a new road, which is the period during which it retains its function. These uncertainties need to be explicitly modeled. The final step in a cost-benefit analysis is to give a policy recommendation based on the result of the evaluation as well as the uncertainties associated with the result.

What Do We Mean by Social Welfare?

The aim with a cost-benefit analysis is to evaluate the welfare effect of a policy. This requires a definition of what is meant by social welfare in an economic framework. The meaning of a welfare improvement is, in its most restricted view, formulated in the Pareto criterion. The Pareto criterion states that a policy that makes at least one individual better off without making any other individual worse off is a Pareto-efficient improvement and increases welfare. However, the Pareto criterion is generally useless as a definition for welfare improvements in a real-world application, because more or less all policies make at least someone worse off. It may also be criticized on ethical grounds; consider a vaccine that would save 1 million lives in sub-Saharan Africa but required a €1 tax on someone (a nonaltruistic individual) in Europe or the United States. According to the Pareto criterion, this policy could not be said to increase welfare, but this conclusion would violate the moral values of most individuals.

As a development to the Pareto criterion, the Kaldor-Hicks criterion was formulated, and it may be seen as the foundation of practical cost-benefit analysis. The Kaldor-Hicks criterion is less restrictive than the Pareto criterion and may be interpreted such that a policy is considered to increase welfare if the winners from a policy are made so much better off that they can fully (hypothetically) compensate the losers and still gain from the policy (potential Pareto improvement). This implies that every Pareto improvement is a Kaldor-Hicks improvement, but the reverse is not necessarily true. The compensation from the winners to the losers is a hypothetical test, and the compensation does not need to be enforced in reality (which distinguishes it from the Pareto criterion). In the example above, the vaccine to save 1 million lives would pass the Kaldor-Hicks criterion, if those who were saved would be hypothetically willing to compensate the European taxpayer with a payment of at least €1. Cost-benefit analysis is a test of the Kaldor-Hicks criterion, translating all the benefits and the costs into a monetary metric. The Kaldor-Hicks criterion also implies that we need to collect information only on aggregate benefits and costs of a policy; we do not need to bother ourselves determining which individuals are actually winning or losing from a policy.

Valuing Benefits and Costs

Performing a cost-benefit analysis of a policy requires that all benefits and costs of the policy be summed up in monetary terms. There are two principal ways of measuring the sum of benefits in a cost-benefit analysis: willingness to pay (WTP) and willingness to accept (WTA). Both WTP and WTA are meant to value how much a certain policy is worth to an individual in monetary terms. The WTP of a policy may be characterized as an individual’s maximum willingness to pay, such that he or she is indifferent to whether the policy is implemented— that is, if it is implemented, utility is the same after the policy as before the policy. The WTA of a policy may instead be characterized as the lowest monetary sum that an individual accepts instead of having the investment implemented—that is, utility is the same when receiving the money as it would have been if the investment had been implemented. WTP and WTA for a policy are expected to differ (WTP < WTA) because of the income effect. Robert D. Willig (1976) shows, using plausible assumptions, that WTP and WTA should not differ from each other by more than a few percentage points. But a lot of research has documented that WTP and WTA usually differ a lot for the same policy, with WTA being significantly higher than WTP. Several hypotheses have been put forward trying to explain this discrepancy, one frequent hypothesis being the existence of an endowment effect (Kahneman, Knetsch, & Thaler, 1990). An endowment effect states that individuals value a good significantly more once they own it, which would create a gap between WTP and WTA for an identical good. In several circumstances, it is often more problematic to estimate WTA for a good, especially in valuation of nonmarket goods, implying that it is more common to use the concept of WTP in cost-benefit analyses.

The cost of a certain policy is based on the opportunity cost concept—that is, the value of the best alternative option that the resources could be devoted to instead. The opportunity cost is derived based on companies’ marginal cost curve—that is, the cost that is associated with increasing output with one unit. This should not be equalized to accounting costs, which do not necessarily tell us the economic cost of an activity. As an example, going to a 2-year MBA program has some direct costs (accounting costs), such as tuition fees, books, and travel costs to attend lectures, but also indirect costs, such as the money that one could earn in a job if not attending the MBA program. The economic cost of an activity is the sum of direct and indirect costs.

Valuation of Market Goods

To estimate benefits and costs of a policy, the natural starting point is to examine whether market prices exist that may be used. If the market is characterized by perfect competition, or a reasonable approximation of perfect competition, and no external effects exist, the market price can give us information about the willingness to pay for a marginal change of a good. For example, if we need to use production factor X for a certain investment and our demand for X will not affect the market price, we can use the market price as a cost measure. The total cost of the production factor in the cost-benefit analysis is, then, the market price multiplied by the number of units used. However, if the policy will also affect the market equilibrium, we cannot simply use the market price in our analysis. Figure 1 shows the difference.

Initial market equilibrium is at price P0 and quantity Q0. Imagine that a new policy will lead to a decrease in the market price to P1, which increases quantity consumed to Q1. The total benefits of this policy include the increased consumer surplus for the original consumers (rectangular area P0P,AC) as well as the benefit of the new consumers (triangle area ABC). Hence, the total benefit may be represented by the area P0P,AB, which shows that when a policy has a nonmarginal effect on the market price, we cannot simply use the prepolicy market price in a cost-benefit analysis.

Figure 1.   Measuring Benefits and Costs for Nonmarginal Changes in Quantity

Other problems to note when using market data as information on prices in a cost-benefit analysis include market imperfections, such as tax distortions and externalities. The existence of taxes implies that there are several market prices, including or excluding taxes. An easy rule of thumb is to calculate prices including taxes, which implies that prices are expressed as consumer prices rather than producer prices. This implies that, for example, labor costs should be calculated as gross wages plus other social benefits or taxes that the consumer (or employer) has to pay. There is also another important effect of taxes that needs to be considered. Usually, cost-benefit analyses are performed for public projects, which often are paid for by taxes that lead to distortions in the economy. Distortions are created because not all activities are taxed, such as leisure. This implies that taxes on labor incomes change the relative prices between labor and leisure and lead to economic inefficiency. The distortion should normally be included in a cost-benefit analysis. As an example, in Swedish cost-benefit analysis, the recommendation is to include a distortion cost of 30% of direct costs (including taxes).

A final complicating note is that theoretically it may be that the correct benefit measure is the option price of a policy, which consists of the expected surplus (as outlined above) as well as the option value of a policy. Option value is the value that individuals are willing to pay, above the expected value of actually consuming the good, to have the option of consuming the good at some point in time (Weisbrod, 1964). For example, investing in a new national park includes the expected surplus of actually going to the park for the visitors. But it may also include a willingness to pay reflecting that the national park provides an option to go there, even if an individual will never actually go. In practical applications, it is common to exclude option value in the benefit calculations. There are several arguments for this; for example, it is difficult to measure and separate option values from other types of values or attitudes that may not be relevant, such as non-use values (non-use value refers to value an individual may place on, e.g., saving the rain forest in a specific region in South America, even if the individual knows that he or she will never go there—an intrinsic value). In addition to option value, sometimes it is also argued that a quasi-option value (sometimes referred to as real option) should be included in a project evaluation. The quasioption value refers to the willingness to pay to avoid an irreversible commitment to the project right now, given expectations of future growth in knowledge relevant to the consequences of the project. It is not particularly common to include quasi-option value in cost-benefit analyses.

Valuation of Nonmarket Goods

A common difficulty with a cost-benefit analysis is the fact that the policy to be evaluated includes a nonmarket good that is publicly provided. This implies that there are no market data to use. Examples of nonmarket goods that are common in cost-benefit analyses of public policy are values of safety, time, environmental goods, and pollution. Generally, there are two main approaches available for estimating monetary values of nonmarket goods: (1) revealed preference (RP) methods and (2) stated preference (SP) methods. RP methods use actual behavior and try to estimate implicit values of WTP or WTA. SP methods use surveys and experiments where individuals are asked to make hypothetical choices between different policy alternatives. Based on these choices, the researcher can estimate WTP or WTA.

Revealed Preference Method

Revealed preference techniques can be used to elicit willingness to pay when there is market information about behavior that at least indirectly includes the good that the analyst is interested in evaluating.

One revealed preference approach is hedonic pricing (Rosen, 1974). Imagine that we would like to estimate the willingness to pay to avoid traffic noise; we may be able look at the housing market in a city to accomplish this. The price of a house depends on many different characteristics: size, neighborhood, number of bedrooms and bathrooms, construction year, and so on. But another important determinant may be the level of noise—that is, a house located close to a heavily trafficked highway will generally be less expensive than an identical house located in a noise-free environment. The hedonic pricing approach uses this intuition and performs a regression analysis where the outcome is the market value of a house including several relevant characteristics as determinants of the house price (including the noise level, measured in decibels). The results from such a statistical analysis can in a second step tell us the impact of the noise level on the market price, holding other important factors constant. For example, Nils Soguel (1994) uses data on monthly rent for housing in the city of Neuchatel in Switzerland and included factors measuring the structure and condition of the building, several apartment-specific factors, and the location of the property. Based on a hedonic pricing approach, it shows that a one-unit increase in decibel led to a reduction in rents by 0.91%. Hence, using this approach, it is possible to estimate the economic value of noise.

Another revealed preference approach is the travel-cost method (see, e.g., Cicchetti, Freeman, Haveman, & Knetsch, 1971). This method uses the fact that individuals can reveal the value of a good by the amount of time they are willing to devote to its consumption. For example, if an individual pays $10 for a train ride that takes 30 minutes to visit a national park, we can use this information to indirectly estimate the lower bound of the value that the individual assigns the park. If the value of time for the individual is $20/hour, the individual is at least willing to spend the train fare of $10 plus $20 for the pure time cost (60 minutes back and forth) to visit the park—that is, a total sum of $30. Using this approach on a large set of individuals (or based on average data from different cities or regions), it is possible to estimate the total consumer surplus associated with the national park.

Stated Preference Method

In many cases, it may not be possible to use revealed preference methods. Further, it should be noted that revealed preference methods assume that individuals (on average) have reasonable knowledge of different product characteristics for a hedonic pricing approach to give reliable estimates. In many cases, this condition may not be fulfilled. A possible option is then to turn to stated preference methods, which are based on hypothetical questions designed such that individuals should reveal the value they would assign to a good if it were implemented in the real world (Bateman et al., 2004). There are two common approaches in the stated preference literature: (1) contingent valuation (CV) and (2) choice modeling (CM). A CV survey describes a scenario to the respondent—for example, a proposed policy of investing in a new railway line— and asks the individual about his or her willingness to pay. A common recommendation is to use a single dichotomous-choice question—that is, respondents are asked whether they would be willing to pay $X for a project—and use a coercive payment mechanism (e.g., a tax raise) for the new public good (Carson & Groves, 2007). The cost of the project is varied in different subsamples of the study, which makes it possible to estimate the willingness to pay (demand curve) for the project using econometric analysis.

In a CM framework, a single respondent is asked to choose between different alternatives where different characteristics of a specific good are altered. For example, the respondent may choose between Project A, Project B, and status quo. Project A and Project B may be two different railway line investments that differ with respect to commute time, safety, environmental pollution, and cost. Using econometric techniques, it is possible to estimate the marginal willingness to pay for all these different attributes using the choices made by the respondents.

Stated preference methods have the advantage that it is possible to directly value all types of nonmarket goods, but the reliability of willingness to pay estimates is also questioned by many economists. Problems include individuals’ tendency to overestimate their willingness to pay in a hypothetical scenario compared to a real market scenario, referred to as hypothetical bias (Harrison & Rutstrom, 2008). There are also many studies that highlight the problem of scope bias (Fischhoff & Frederick, 1998), which refers to the fact that willingness to pay is often insensitive to the amount of goods being valued—for example, willingness to pay is the same for saving one whale as for saving one whale and one panda.

Application: The Value of a Statistical Life

Many cost-benefit analyses concern public policies with effects on health risks (mortality and morbidity risks). Environmental regulation and infrastructure investment are two examples where policies often have direct impacts on mortality risks, morbidity risks, or both. Hence, we need some approach to monetize health risks. In the United States, an illustrative example can be found in the evaluation of the American Clean Air Act by the Environmental Protection Agency, where 80% of the benefits were made up of the value of reduced mortality risks (Krupnick et al., 2002). In a European example (from Sweden), cost-bene-fit analyses of road investments show that approximately 50% of the benefits consist of mortality and morbidity risk reductions (Persson & Lindqvist, 2003).

In the literature of the last 20 to 30 years, the concept used to monetize the benefit of reduced mortality risk is the value of a statistical life (VSL). It may be described in the following way:

Suppose that you were faced with a 1/10,000 risk of death. This is a one-time-only risk that will not be repeated. The death is immediate and painless. The magnitude of this probability is comparable to the annual occupational fatality risk facing a typical American worker and about half the annual risk of being killed in a motor vehicle accident. If you faced such a risk, how much would you pay to eliminate it? (Viscusi, 1998, p. 45)

Let us assume that a certain individual is willing to pay $100 to eliminate this risk. Using this information on WTP, the value of a statistical life is then based on the concept of adding up this total willingness to pay for a risk reduction of 1 in 10,000 to 1. Hence, in this example, it implies that the estimate for the value of a statistical life is equal to $100 x 10,000 = $1 million (VSL = WTP / A risk). This implies that a policy that prevents one premature fatality increases social welfare as long as the cost is less than $1 million.

What value should be used in a cost-benefit analysis? There is no market where we explicitly trade with small changes in mortality risks. Rather, researchers have been forced to turn to RP and SP methods to estimate VSL. The most common RP approach has been to use labor market data to estimate the wage premium demanded for accepting a riskier job (hedonic pricing). The idea behind these studies is that a more dangerous job will have to be more attractive in other dimensions to attract competent workers, and one such dimension is higher pay. Hence, by controlling for other important determinants of the wage, it is possible to separate the effect that is due to a higher on-the-job fatality risk. This approach has been particularly popular in the United States; see W. Kip Viscusi and Joseph Aldy (2003) for a survey of several papers using this approach.

SP approaches to estimate VSL are also frequent. Primarily, they have been performed using the contingent valuation approach. For example, a survey might begin with a description of the current state of the world regarding traffic accidents in a certain municipality, region, or country. The respondent might be told that in a population of 100,000 individuals, on average, 5 people will die in a traffic accident the next year. After this description, the respondent might be asked to consider a road safety investment that would, on average, reduce this mortality risk from 5 in 100,000 to 4 in 100,000—that is, 1 fewer individual killed per 100,000 individuals. To elicit the preferences of the respondent, the following question may be asked: Would you be willing to pay $500 in a tax raise to have this traffic safety program implemented? The respondent then ticks a box indicating yes or no. Other respondents are given other costs of the project, which gives the researcher the possibility of estimating a demand curve for the mortality risk reduction.

In the United States, the Environmental Protection Agency recommends a VSL estimate of €6.9 million for cost-benefit analyses in the environmental sector. In Europe, for the Clean Air for Europe (CAFE) program, a VSL (mean) of €2 million (approx. $2.7 million) is suggested (Hurley et al., 2005). Theoretically, higher income and higher baseline risk should be associated with a higher VSL (although this can hardly explain the large differences between the estimates used by the United States and the European Union). In the transport sector, there are also international differences. Among European countries, Norway recommends a VSL estimate for infrastructure investments of approximately €2.9 million; the United Kingdom, €1.8 million; Germany, €1.6 million; Italy, €1.4 million; and Spain, €1.1 million (HEATCO, 2006).

For many individuals, it is offensive to suggest that the value of life should be assigned a monetary value, and there is some critique from the research community (Broome, 1978). However, it needs to be acknowledged that in estimating WTP for small mortality risk reductions for each individual (hence the term statistical life), no one is trying to value the life of an identified individual. Moreover, these decisions have to be made, and we make them daily on an individual basis. Public policy decision making on topics that have impacts on mortality risks will always implicitly value a prevented fatality. Using the concept VSL in cost-benefit analysis, economists are merely trying to make these decisions explicit and base them on a rational decision principle.

Discounting

As stated in the introduction, benefits and costs associated with a policy that occur at different times need to be expressed in a common metric. This metric is the present value of benefits and the present value of costs. Practically, discounting into present value is calculated as PV = Bt / (1 + SDR)t, where PV is the present value of a benefit (Bt) occurring in year t in the future. SDR is the social discount rate. For example, with a social discount rate of 3%, a benefit of $100 occurring in 5 years has a present value of $100 / (1 + 0.03)5 = $86.26. Traditionally, there have been two main approaches to choosing an appropriate social discount rate: (1) the social opportunity rate cost of capital, and (2) social time preference rate. The former can be seen as the opportunity cost of capital used for a certain policy. Imagine that a road safety investment has a cost of $100; this money could instead be placed in a (more or less) risk-free government bond at a real interest rate of perhaps 5%. This would be the opportunity cost of the capital used for the public policy. The social time preference rate approach can be formulated as in the optimal growth model (Ramsey, 1928) outlining the long-run equilibrium return of capital: SDR = p + m x g, where p is the pure time preference of individuals, p is the income elasticity, and g is the growth rate of the economy. This captures both that individuals tend to receive $1 today rather than in a year (p), and it reflects that as individuals grow richer, each additional $1 is worth less to them (m).

The actual discount rate used in economic evaluation often has a major impact on the result. Consider Table 1, which shows the present value of $1 million in 10, 30, 50, and 100 years in the future with discount rates of 1%, 3%, 5%, and 10%. As an example, with a discount rate of 1%, the present value of $1,000,000 occurring in 10 years is $905,287. The present value if using a discount rate of 5% is instead $613,913.

Table 1 can be used to show how important the discount rate is for the discussion regarding what to do about global warming. The predicted costs of global warming are assumed to lie quite distantly in the future. The effect of different discount rates will be relatively larger the more distant in the future the benefit or cost will take place. An environmental cost of $1 million occurring in 100 years is equal to only $7,604 in present value if using a discount rate of 5%. If using a discount rate of 1%, it is $369,711. A policy that would be paid for today to eliminate this cost in 100 years would be increasing social welfare if it cost less than $7,604 using the higher discount rate (5%) or would be increasing social welfare if it cost less than $369,711 using the lower discount rate (1%). Hence, it is obvious that the conclusion on how much of current GDP we should spend to decrease costs of global warming occurring in the distant future will be highly dependent on the chosen discount rate used in the cost-benefit analysis.

In 2006, a comprehensive study on the economics of climate change was presented by the British government: the Stern Review (Stern, 2007). The review argues that the appropriate discount rate for climate policy is 1.4%. Stern argues that because global warming is an issue affecting many generations, the pure time preference (p) should be very low (0.1), and he assumes an income elasticity (m) of 1 and a growth rate (g) of 1.3; this implies SDR = 0.1 + 1 x 1.3 = 1.4. This is a relatively low discount rate compared to what most governments recommend for standard cost-benefit analyses around the world for projects with shorter life spans than policies to combat global warming. The Stern Review has also been criticized by other economists, who argue that such a low discount rate is not ethically defensible and has no connection to market data or behavior (Nordhaus, 2007). Weitzman (2007) argues that a more appropriate assumption is that p=2, m = 2, and g = 2, which would give a social discount rate of 6%. The debate about the correct social discount rate has been a very public question in discussions about global warming policy—that is, how much, how fast, and how costly should measures taken today to reduce carbon emissions be?

Table 1.   Present Value of $1 Million Under Different Time Horizons and Discount Rates

There really exists no consensus regarding the correct discount rate, and there probably never will. Different government authorities around the world propose different social discount rates. The European Union demands that cost-benefit analyses be conducted for projects that imply important budget consumption, and the European Commission proposes a social discount rate of 5%. In the so-called Green Book in the United Kingdom, a social discount rate of 3.5% is proposed. In France, the Commisariat Général du Plan proposes a discount rate of 4%. In the United States, there are somewhat different proposals for different sectors, but the Office of Management and Budget recommends a social discount rate of 7% (Rambaud & Torrecillas, 2006). Several of the recommendations are also indicating that the social discount rate should be dependent on the time horizon of the project; in the United Kingdom, the social discount rate is proposed to be 3.5% for year 0 to 30, 3% for year 31 to 75, and decreasing down to 1% for policies with a life span of more than 301 years.

Sensitivity Analysis

There are often large uncertainties in a cost-benefit analysis, regarding parameter estimates of benefits and costs. It has been shown that, especially for large projects, costs are often underestimated and benefits are sometimes exaggerated, making projects look more beneficial than they actually are (Flyvbjerg, Holm, & Buhl, 2002, 2005). These types of uncertainties need to be explicitly discussed and evaluated in the analysis. One common approach to deal with uncertainty in a cost-benefit analysis is to perform sensitivity analyses. There are different approaches regarding how to conduct a sensitivity analysis. Partial sensitivity analysis involves changing different parameter estimates and examines how it affects the net present value of the policy. Examples include using different discount rates and different parameter values of the value of a statistical life. Another approach is the so-called worst- and best-case analysis. Imagine that the benefits are uncertain but that an interval can be roughly estimated—for example, the benefit of improving environmental quality will be in the interval $100,000 to $150,000. A worst-case analysis implies taking the lowest bound of all beneficial parameter estimates. A best-case analysis implies the opposite. These types of sensitivity analyses may be interesting for a risk-averse decision maker and also give information about the lowest benefit (or largest loss) for a given project.

The downside to partial sensitivity analysis and worst-and best-case scenarios is that they do not take all available information about the parameters into consideration. Further, they do not give any information about the variance of the net present value of a project. For example, if two projects give similar net present value, decision makers may be more interested in the project with the lowest variance around the outcome. This requires the use of Monte Carlo sensitivity analysis. This is based on simulations where economists make assumptions about the statistical distribution of different parameters and perform repeated draws of different parameter values, each leading to a different net present value. This can give an overview of the distribution of the uncertainty of the project. A standard approach to visually describe the results from a Monte Carlo sensitivity analysis is to display the results in a histogram that shows mean net present value, sample variance, and standard error.

An Application

To end this overview, a cost-benefit analysis of the Stockholm congestion charging policy is described (Eliasson, 2009). The Stockholm road congestion charging system is based on a cordon around central parts of Stockholm (capital of Sweden), with a road toll between 6:30 a.m. and 6:30 p.m. weekdays (higher charge during peak hours). The aim with the charging system is to reduce congestion and increase the reliability of travel times. Positive effects on safety and the environment are also expected. The cost-benefit analysis has the particular advantage of being based on observed traffic behavior, rather than simulations and forecasts (this is possible because the charging system was introduced during a trial period of 6 months). Using the six steps in a cost-benefit analysis as outlined in the introduction:

• The first step involves defining the relevant population. The Stockholm congestion charging policy is mainly relevant for the population in the region of Stockholm, but because the cost-benefit analysis is based on actual behavior and data, it will therefore include benefits and costs of users of the roads in Stockholm, which will include various types of visitors as well. Hence, the relevant population is all the users of the roads.
• The second step in a cost-benefit analysis is to identify the relevant consequences associated with the policy. The following main benefits are associated with the policy:

(a) reduction in travel times due to decreased congestion, (b) increased reliability in travel times, (c) reductions of carbon dioxide and health-related emissions due to the decrease in traffic volume, and (d) increased road safety due to decrease in traffic volume. It could be hypothesized that the system would have effects on decisions where to locate, the regional economy, and retail sales, but it has been judged that these effects will be very small. Negative effects are (a) investment and startup costs, and (b) yearly operation costs.

• The third step in the cost-benefit analysis is to monetize all the benefits and costs associated with the policy. Table 2 summarizes the consequences and shows their monetary benefits and costs. Some of the smaller benefits and costs in the analysis are not described here; refer to the reference for a more detailed description.

Table 2.   Annual Benefits and Costs of the Stockholm Road Charging System

Table 2 shows the annual benefits and costs of the system. The magnitude of the effects on travel time, standard deviation of travel time, and so on is based on large computer estimations of the traffic measurements on 189 links to calibrate origin-destination (OD) matrices for the case with and without the charging system. The investment cost is not listed in Table 2 but was estimated at 1.9 million SEK.

• The next step consists of calculating the net present value of all benefits and costs based on the annual estimates. It is not obvious which time horizon should be used for the project, but technical data and past experience indicated that it was reasonable to make a conservative assumption that the system would have an economic life span of 20 years. Hence, the benefits and operating costs in year 2, 3, … , 20 have to be discounted to a present value. The Swedish National Road Administration argues that the social discount rate should be 4%. Hence, the present value of the net social benefits in year 20 is 654 / (1.04)20 = 298 million SEK. These calculations are performed for benefits and costs in years 1 through 20.
• Discount all annual benefits and costs to present values, as in Step 4, showing that the total social surplus (after deducting the investment costs) is approximately

6.3 billion SEK (approx. $800 million). Expressing it as a payback estimate, this means that the policy will take 4 years before the investment costs are fully repaid. It is also explicitly discussed that some consequences were deemed too difficult to include in the calculations, such as the effects on noise, labor market, time costs for users, quicker bus journeys, and so on.

• The sixth step in a cost-benefit analysis is to perform a sensitivity analysis. In this aspect, there is little done in the described analysis. One reason for this is that the actual estimates of the consequences as performed using OD matrices are very time consuming, which more or less implies that because of practical limitations, only one main estimation can be performed. A simple sensitivity analysis is performed assuming increasing benefits over the time horizon. But if any improvement to the cost-benefit analysis should be suggested, it would be to conduct a more detailed sensitivity analysis. The quite straightforward conclusion of the cost-benefit analysis, even though it should have included sensitivity analyses to satisfy our full requirements, is that social welfare will increase because of the charging system.

How should we evaluate a proposed public policy or regulation? A cost-benefit analysis is an approach that includes all relevant consequences of a policy and compares, in monetary units, benefits with costs. If benefits outweigh the costs, the policy is said to increase social welfare. Social welfare is defined using the Hicks-Kaldor criterion, which states that a policy increases welfare if the winners from the policy can compensate the losers from the policy and still be better off than if the policy is not implemented.

To conduct a cost-benefit analysis, one must identify consequences and express them in a monetary metric so that all consequences can be compared in the same unit of measurement. If benefits and costs arise in the future, they should be discounted to present value using a social discount rate. Finally, given the uncertainties involved with estimating consequences of a policy or regulation as well as uncertainties with the monetary estimates of the consequences, a proper cost-benefit analysis should include sensitivity analyses to show how robust the result is.

Finally, considering the definition of social welfare as usually applied in cost-benefit analysis (Hicks-Kaldor criterion), the typical cost-benefit analysis of a project or regulation estimates the effect on economic efficiency. Therefore, even though a very important guide to decision making, in most applications, cost-benefit analysis is often seen as one of several guides to the decision making process. Especially in political decision making, there will be other effects of interest, such as effects on income distribution and geographical distribution of benefits.

Bibliography:

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• Broome, J. (1978). Trying to save a life. Journal of Public Economics, 9, 91-100.
• Carson, R. T., & Groves, T. (2007). Incentive and informational properties of preference questions. Environmental and Resource Economics, 37, 181-210.
• Cicchetti, C. J., Freeman, A. M., Haveman, R. H., & Knetsch, J. L. (1971). On the economics of mass demonstrations: A case study of the November 1969 march on Washington. American Economic Review, 61, 719-724.
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• Flyvbjerg, B., Holm, M. K., & Buhl, S. L. (2002). Underestimating costs in public works projects: Error or lie? Journal of the American Planning Association, 68, 279-295.
• Flyvbjerg, B., Holm, M. K., & Buhl, S. L. (2005). How (in)accurate are demand forecasts in public works projects? The case of transportation. Journal of the American Planning Association, 71, 131-146.
• Harrison, G. W., & Rutström, E. E. (2008). Experimental evidence on the existence of hypothetical bias in value elicitation methods. In C. Plott & V Smith (Eds.), Handbook of experimental economics results (pp. 752-767). New York: Elsevier Science.
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A Psychologist Explains How The ‘Lion’s Gate Portal’ Can Benefit You

Days like 8/8 can benefit you regardless of your belief in them as they create the perfect storm of ... [+] positivity, placebo and manifestation practice.

Research has confirmed time and again that the gaps between psychological science and spirituality are wide. While one uses treatment modalities developed through scientific rigor, the other banks on faith, belief and optimism.

Paradoxically, however, psychological healing often intersects with spirituality in the realm of practice. “Manifestation” exercises such as meditation and chanting, positive visualization, journaling and affirmations are prescribed in both spaces regularly and are often rooted in gaining more knowledge of and control over the subconscious and unconscious mind.

Research published in 2023 also indicates that certain psychological constructs, like being in a “flow state,” mirror spiritual experiences. The study further argues that incorporating spirituality into your life may enhance self-understanding and potential through self-belief, a goal therapists often set for clients they treat.

All of this is to say that there are many paths that lead to a desired destination. Whether you are a realist with elaborate plans for the future or you’re a spiritual soul building a deeper connection with the universe, manifestation exercises can help you break substantial ground on the journey you’re already on.

And while there is no perfect time to start this journey, many swear by certain fated days, meant to be more powerful and “bountiful” than others. Today is supposed to be one such day, marking the opening of the “Lion’s Gate portal.” Here’s the lore behind the popular legend.

The Astrological Tale Behind Lion’s Gate Portal

Spiritual practitioners claim the eighth of August to be the day the universe supposedly opens a cosmic gateway known as the Lion’s Gate Portal. With Sirius rising and the Sun in Leo, believers claim this is a magical window for transformation and manifestation, as if the universe itself is conspiring to grant all wishes.

For those who believe the lore, it presents a tantalizing chance to harness the universe’s supposed powers. Whether it’s celestial truth or just a fanciful story lacking scientific or cosmic corroboration, the intent to start manifesting in your life is never unuseful. Regardless of these beliefs, manifestation can always help people achieve their best potential.

Why Does Manifestation Work Well With Spirituality?

While they may use vastly different language, construct different arguments and are trying to prove different things—spiritual healing and psychological healing often coincide when it comes to execution. Here’s a psychologist’s take on why manifestation works in both worlds:

• The placebo effect of faith and positive outcomes. Research published in Philosophical Transactions of the Royal Society B suggests that belief systems, including spiritual practices, can impact physical health and well-being. Another study examining the Covid-19 pandemic found that patients with higher emotional, social, physical and spiritual resilience experienced less severe symptoms and recovered more quickly, illustrating how faith can influence outcomes through the placebo effect. And finally, in a 2020 article discussing the power of religious practices, psychiatrist Harold Koening notes: “Placebos have been used in medicine since antiquity and may have significantly improved health and quality of life when little was known about the causes of most illnesses. Many outcomes were likely due to the placebo effect, as available treatments were either unproven or later disproven.” In the same vein, practices like manifestation may rely on the placebo effect, where believing in positive outcomes creates a psychological environment that supports achieving those outcomes.
• The powerful role of self-efficacy. Prolific researcher Albert Bandura's work on self-efficacy highlights the power of one’s belief in their own ability to succeed. When individuals engage in manifestation practices, spiritual or not, they are essentially boosting their self-efficacy—which can lead to better performance and greater resilience in the face of challenges. This helps in building a positive self-image and enhances strength to take righteous actions towards one's ambitions.
• Principles of Cognitive Behavioral Therapy (CBT). CBT , a well-established psychological treatment modality, emphasizes the importance of changing negative thought patterns to improve mental health. Manifestation techniques, such as affirmations and visualization, align closely with CBT principles by encouraging individuals to focus on positive thoughts and outcomes, thereby reducing anxiety and self-sabotaging thoughts.

How You Can Harness The “Magic” Of Days Like 8/8

Whether ordained by the universe or not, there may not be a better time than now to channelize your mental and spiritual energy toward manifesting the goals you desire to achieve. Here’s why the efficacy of these tools can feel like magic:

• Meditation and visualization. Meditation and visualization are powerful tools that help individuals focus their intentions and reduce stress. Research led by epidemiologists at West Virginia University shows that regular meditation can enhance cognitive function and emotional regulation.
• Journaling. Writing down aspirations and goals can clarify intentions and create a tangible blueprint for success. Journaling has been shown to improve mental health by allowing individuals to process emotions and articulate their own thoughts.
• Environmental enhancements. Creating a conducive environment for manifestation, such as lighting candles or using fragrances, can enhance mood and focus. Research published in Scientia Pharmaceutica suggests that certain olfactory stimulation can positively affect mood and cognitive function.
• Affirmations. Repeating affirmations can reinforce positive beliefs and motivate individuals to pursue their goals. A 2015 study indicates that affirmations, when practiced consistently and spoken as if true, can improve performance and self-perception through a sense of achieving rewards.

While the myths surrounding events like the Lion’s Gate portal may blend astrological assumptions into daily life, the practice of manifestation itself holds significant psychological value at all times in life. The power of intention, belief and structured practice can have profound effects on cognitive health and personal growth. By understanding and harnessing these psychological techniques, individuals can achieve positive transformations, regardless of their spiritual beliefs.

Test your levels of spirituality by taking the science-backed Ego Dissolution Scale, here .

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Horses can plan and strategise, new study shows

You can lead a horse to water and, it turns out, convince it to drink if the reward is great enough, researchers have found.

A new study has suggested horses are more intelligent than previously thought, having been observed to quickly adapt to a treat-based game with changing rules.

Researchers from Nottingham Trent University (NTU) said they were surprised by how the horses quickly grasped the game, busting previous theories that equine brains respond only to immediate stimuli and are not complex enough to strategise.

The new findings could lead to more humane horse training regimes and improvements to their welfare, researchers said.

The study involved 20 horses, who first were rewarded with a treat for touching a piece of card with their nose.

In the second stage, a "stop light" was introduced, and the rule was changed so that the reward was only given if they touched the card while the light was off.

This did not alter the behaviour of the horses, as they were observed touching the card regardless of the status of the light. That is, until the rules changed for a third time.

In the final stage, researchers introduced a penalty of a 10-second timeout for touching the card while the stop light was on.

The team observed a rapid adjustment to the horses' behaviour now there was a cost to getting it wrong, all of them quickly learning to play by the rules to avoid the timeout, researchers said.

"We were expecting horses' performance to improve when we introduced the time-out, but were surprised by how immediate and significant the improvement was," lead researcher Louise Evans said.

The researchers believe the fact the horses adapted so quickly indicates they understood the rule of the stop light the entire time, but had no reason to follow the rule when there was no consequence for getting it wrong.

The study, published in the Applied Animal Behaviour Science journal shows that horses are more cognitively advanced than they are given credit for, Dr Carrie Ijichi, a senior equine researcher at NTU said.

"This teaches us that we shouldn't make assumptions about animal intelligence or sentience based on whether they are 'built' just like us," she said.

Deserted: The U.S. Military's Sexual Assault Crisis as a Cost of War

Over the past decade, the U.S. military has implemented policies to promote gender equality, notably lifting the ban on women in combat roles in 2013 and opening all military jobs to women by 2016. Yet, even as U.S. military policy reforms during the “War on Terror” appear to reflect greater equality, violent patterns of abuse and misogyny continued within military workplaces.

This author of this report found that sexual assault prevalence in the military is likely two to four times higher than official government estimations. Based on a comparison of available data collected by the U.S. Department of Defense to independent data, the research estimates there were 75,569 cases of sexual assault in 2021 and 73,695 cases in 2023. On average, over the course of the war in Afghanistan, 24 percent of active-duty women and 1.9 percent of active-duty men experienced sexual assault. The report highlights how experiences of gender inequality are most pronounced for women of color, who experience intersecting forms of racism and sexism and are one of the fastest-growing populations within the military. Independent data also confirm queer and trans service members’ disproportionately greater risk for sexual assault.

The report notes that during the post-9/11 wars, the prioritization of force readiness above all else allowed the problem of sexual assault to fester, papering over internal violence and gender inequalities within military institutions.

READ FULL PAPER >

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Enhanced benefits to help reduce nurse turnover

A significant shortage of registered nurses is projected through 2036. As health care administrators face challenges in both hiring and retaining nurses, there are opportunities to offer enhanced employee benefits to reduce nurse turnover.

Nurses are in high demand, and it doesn’t look to be letting up any time soon. Health care organizations not only need more nurses but also want them to stay longer, appreciating the significant contributions they make to patient care .

The supply and demand of registered nurses 2021 - 2036

Department of health and Human Services, Health Resources and Services Administration, Health Workforce Projections. https://data.hrsa.gov/topics/health-workforce/workforce-projections (Under Occupation, select Registered Nurses).

Nurse turnover accelerated by pandemic

The cost of nurse turnover.

This means nearly three months in which more than just the budget is impacted, including staff morale and patient care.

Tackling nurse turnover with enhanced benefits

There are ways to help reduce turnover. The first thing is to dig into your specific cost to replace a nurse. This budget can be significant for many healthcare organizations. If each open RN position costs roughly $50,000 to replace, a quick look at the career page of a healthcare provider will be eye-opening. With average turnover over 100% in a five-year period, many of these RN positions must get filled again and again. If an organization has 100 open positions, that's roughly $5M they may need to spend on hiring, training, lost production, overtime, traveling nurses, and more. Instead, work with human resources and benefits advisors/consultants for ways to proactively redirect some of that budget towards nurse retention efforts.

Ways to help reduce nurse turnover:

• Student loan repayment assistance Given the educational needs of the healthcare workforce, student debt weighs heavily on their budget. Updated legislation (SECURE 2.0) lets employers match student loan payments with contributions to the employee’s retirement account.
• Sign-on bonuses These should have a repayment period if someone leaves, to influence not only recruitment, but to encourage as much tenure as possible.
• Retention bonuses Use data to encourage a length of time where the bonus outweighs the cost of turnover.
• Combined bonuses Consider using a sign-on bonus and retention bonus together to both provide quickly attainable benefits and longer retention. For example, if a healthcare organization wishes to keep nurses beyond three years, they could provide a sign-on bonus that would need to be paid back until the person worked at least 12 months, combined with a retention bonus that pays out at 24 months and again at 36 months. The idea being to provide easily attainable benefits that are always close to paying out.
• Home down payment assistance programs A unique spin on retention bonuses, this can have the added benefit of nurses establishing roots in the community.
• Enhanced retirement benefits Consider alternative structures, such as match contributions that increase with service or a combination of age and service to reward industry experience. Some nurse positions may even be eligible for non-qualified plan benefits. They could also see an advantage from the addition of a post-tax contribution allowed within the plan, to enable backdoor Roth contributions.

Collaborate with a health care retirement professional

Properly benchmarking and understanding the competitive situation an organization faces can be a great first step. It’s important to understand what the organization may need to provide in order to properly recruit and retain the talent they need.

Using the ideas above, there’s an opportunity to implement a targeted benefits strategy to help improve your recruiting efforts, improve the quality of patient care, support nurses’ financial well-being, and enhance the overall resilience of the organization.

Health care is an intricate business, and encouraging nurses to stay with your organization can be challenging, but it is possible. It’s important to work with a retirement service provider that understands and has the expertise to consult on options to help deliver the desired results.

Discover more retirement research and insights

Find the latest on plan design, retirement legislation, and pension plans from Principal® thought leaders. Uncover additional ways to use the retirement plan as an incentive that can work for your business and helps your employees save more. Get more insights .

If you’re looking for options that could work in building a more robust retirement plan—reach out to your Principal ® representative.

7 steps to design a benefit plan that you and your employees will love

Use these tips to enhance or extend your benefits and stay ahead of your competitors.

Offer the benefits your employees really want (that you can also afford) with these 5 tips

Discover what employees want to see in a benefits package and how your business can accommodate without overspending.

How to create a voluntary benefits package your employees want and need

A just-for-your-business voluntary benefits package can help recruit and retain employees while you grow your business.

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Study reveals ways in which 40Hz sensory stimulation may preserve brain’s “white matter”

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Early-stage trials in Alzheimer’s disease patients and studies in mouse models of the disease have suggested positive impacts on pathology and symptoms from exposure to light and sound presented at the “gamma” band frequency of 40 hertz (Hz). A new study zeroes in on how 40Hz sensory stimulation helps to sustain an essential process in which the signal-sending branches of neurons, called axons, are wrapped in a fatty insulation called myelin. Often called the brain’s “white matter,” myelin protects axons and insures better electrical signal transmission in brain circuits.

“Previous publications from our lab have mainly focused on neuronal protection,” says Li-Huei Tsai , Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT and senior author of the new open-access study in Nature Communications . Tsai also leads MIT’s Aging Brain Initiative. “But this study shows that it’s not just the gray matter, but also the white matter that’s protected by this method.”

This year Cognito Therapeutics, the spinoff company that licensed MIT’s sensory stimulation technology, published phase II human trial results in the Journal of Alzheimer’s Disease indicating that 40Hz light and sound stimulation significantly slowed the loss of myelin in volunteers with Alzheimer’s. Also this year, Tsai’s lab published a study showing that gamma sensory stimulation helped mice withstand neurological effects of chemotherapy medicines, including by preserving myelin. In the new study, members of Tsai’s lab led by former postdoc Daniela Rodrigues Amorim used a common mouse model of myelin loss — a diet with the chemical cuprizone — to explore how sensory stimulation preserves myelination.

Amorim and Tsai’s team found that 40Hz light and sound not only preserved myelination in the brains of cuprizone-exposed mice, it also appeared to protect oligodendrocytes (the cells that myelinate neural axons), sustain the electrical performance of neurons, and preserve a key marker of axon structural integrity. When the team looked into the molecular underpinnings of these benefits, they found clear signs of specific mechanisms including preservation of neural circuit connections called synapses; a reduction in a cause of oligodendrocyte death called “ferroptosis;” reduced inflammation; and an increase in the ability of microglia brain cells to clean up myelin damage so that new myelin could be restored.

“Gamma stimulation promotes a healthy environment,” says Amorim, who is now a Marie Curie Fellow at the University of Galway in Ireland. “There are several ways we are seeing different effects.”

The findings suggest that gamma sensory stimulation may help not only Alzheimer’s disease patients but also people battling other diseases involving myelin loss, such as multiple sclerosis, the authors wrote in the study.

Maintaining myelin

To conduct the study, Tsai and Amorim’s team fed some male mice a diet with cuprizone and gave other male mice a normal diet for six weeks. Halfway into that period, when cuprizone is known to begin causing its most acute effects on myelination, they exposed some mice from each group to gamma sensory stimulation for the remaining three weeks. In this way they had four groups: completely unaffected mice, mice that received no cuprizone but did get gamma stimulation, mice that received cuprizone and constant (but not 40Hz) light and sound as a control, and mice that received cuprizone and also gamma stimulation.

After the six weeks elapsed, the scientists measured signs of myelination throughout the brains of the mice in each group. Mice that weren’t fed cuprizone maintained healthy levels, as expected. Mice that were fed cuprizone and didn’t receive 40Hz gamma sensory stimulation showed drastic levels of myelin loss. Cuprizone-fed mice that received 40Hz stimulation retained significantly more myelin, rivaling the health of mice never fed cuprizone by some, but not all, measures.

The researchers also looked at numbers of oligodendrocytes to see if they survived better with sensory stimulation. Several measures revealed that in mice fed cuprizone, oligodendrocytes in the corpus callosum region of the brain (a key point for the transit of neural signals because it connects the brain’s hemispheres) were markedly reduced. But in mice fed cuprizone and also treated with gamma stimulation, the number of cells were much closer to healthy levels.

Electrophysiological tests among neural axons in the corpus callosum showed that gamma sensory stimulation was associated with improved electrical performance in cuprizone-fed mice who received gamma stimulation compared to cuprizone-fed mice left untreated by 40Hz stimulation. And when researchers looked in the anterior cingulate cortex region of the brain, they saw that MAP2, a protein that signals the structural integrity of axons, was much better preserved in mice that received cuprizone and gamma stimulation compared to cuprizone-fed mice who did not.

A key goal of the study was to identify possible ways in which 40Hz sensory stimulation may protect myelin.

To find out, the researchers conducted a sweeping assessment of protein expression in each mouse group and identified which proteins were differentially expressed based on cuprizone diet and exposure to gamma frequency stimulation. The analysis revealed distinct sets of effects between the cuprizone mice exposed to control stimulation and cuprizone-plus-gamma mice.

A highlight of one set of effects was the increase in MAP2 in gamma-treated cuprizone-fed mice. A highlight of another set was that cuprizone mice who received control stimulation showed a substantial deficit in expression of proteins associated with synapses. The gamma-treated cuprizone-fed mice did not show any significant loss, mirroring results in a 2019 Alzheimer’s 40Hz study that showed synaptic preservation. This result is important, the researchers wrote, because neural circuit activity, which depends on maintaining synapses, is associated with preserving myelin. They confirmed the protein expression results by looking directly at brain tissues.

Another set of protein expression results hinted at another important mechanism: ferroptosis. This phenomenon, in which errant metabolism of iron leads to a lethal buildup of reactive oxygen species in cells, is a known problem for oligodendrocytes in the cuprizone mouse model. Among the signs was an increase in cuprizone-fed, control stimulation mice in expression of the protein HMGB1, which is a marker of ferroptosis-associated damage that triggers an inflammatory response. Gamma stimulation, however, reduced levels of HMGB1.

Looking more deeply at the cellular and molecular response to cuprizone demyelination and the effects of gamma stimulation, the team assessed gene expression using single-cell RNA sequencing technology. They found that astrocytes and microglia became very inflammatory in cuprizone-control mice but gamma stimulation calmed that response. Fewer cells became inflammatory and direct observations of tissue showed that microglia became more proficient at clearing away myelin debris, a key step in effecting repairs.

The team also learned more about how oligodendrocytes in cuprizone-fed mice exposed to 40Hz sensory stimulation managed to survive better. Expression of protective proteins such as HSP70 increased and as did expression of GPX4, a master regulator of processes that constrain ferroptosis.

In addition to Amorim and Tsai, the paper’s other authors are Lorenzo Bozzelli, TaeHyun Kim, Liwang Liu, Oliver Gibson, Cheng-Yi Yang, Mitch Murdock, Fabiola Galiana-Meléndez, Brooke Schatz, Alexis Davison, Md Rezaul Islam, Dong Shin Park, Ravikiran M. Raju, Fatema Abdurrob, Alissa J. Nelson, Jian Min Ren, Vicky Yang and Matthew P. Stokes.

Fundacion Bancaria la Caixa, The JPB Foundation, The Picower Institute for Learning and Memory, the Carol and Gene Ludwig Family Foundation, Lester A. Gimpelson, Eduardo Eurnekian, The Dolby Family, Kathy and Miguel Octavio, the Marc Haas Foundation, Ben Lenail and Laurie Yoler, and the U.S. National Institutes of Health provided funding for the study.

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