Acost-benefit analysis of a project or a change in government policy sums all the benefits resulting from the project or policy and subtracts all the associated costs. Doing this requires that the values of all these benefits and costs are measured in monetary terms. Although this is often difficult to accomplish in practice, in principle it would be relatively straightforward if the changes in consumer surplus and producer surplus resulting from a change in government policy, as well as the policy's effects on government revenues, could be determined. Under most circumstances, as indicated in Chapter 3, it is changes in these values that provide conceptually correct measures of the monetary value of a government policy's benefits and costs.

This chapter and the next illustrate how changes in consumer surplus, producer surplus, and net government revenues could be readily estimated if all the pertinent market demand and supply curves were known. This chapter focuses on demand and supply curves in primary markets, while Chapter 5 examines demand and supply curves in secondary markets. Primary markets refer to markets that are directly affected by a policy or project; for example, if a city builds a new subway system, the primary markets are the market for public transportation and the market for materials used to build the subway. Secondary markets are markets that are indirectly affected—for example, the market for gasoline if some commuters switch from driving to riding the new subway.

The chapter begins with a brief discussion of why real CBA studies often fail to use conceptually correct measures of benefits and costs and what the implications are of this. We then examine how the effects of government policies in primary markets can be valued. In doing so, we emphasize the concept of willingness to pay (WTP) and, thus, demand curves and consumer surplus. We next describe the valuation of resources purchased in primary markets as inputs for government projects, stressing the concept of opportunity costs and, hence, the use of supply curves and producer surplus. Throughout this chapter we ignore the marginal excess tax burden.

This chapter also provides brief explanations of common types of market failures including monopoly, externalities, information asymmetries, public goods, and addictive goods.

Both private and public decisions can have important consequences that extend over time. When consumers buy houses, automobiles, or education, they generally expect to derive benefits and incur costs over a number of years. When the government builds a dam, subsidizes job training, regulates carbon dioxide emissions, or leases the outer continental shelf for oil exploration, it also sets in motion impacts that extend over many years. Often, analysts have to compare projects with benefits and costs that arise in different time periods. Formally, they have to make intertemporal (across time) comparisons. To do this, analysts discount future costs and benefits so that all costs and benefits are in a common metric—the present value. They can then measure and compare the net social benefits of each policy alternative using the net present value criterion.

This chapter deals with the practical techniques needed to compute the net present value of a project (or policy). It assumes that the social discount rate, the rate at which analysts should discount the future benefits and costs of a project, is known. As we discuss in Chapter 10, some controversy remains over the appropriate value of the social discount rate. In practice, though, oversight agencies, such as the Office of Management and Budget in the United States, Her Majesty's Treasury in the United Kingdom, or the Treasury Board in Canada, often specify the discount rate that analysts should use.

The sections of this chapter cover the following topics: the basics of discounting, compounding and discounting over multiple years, the timing of benefits and costs, comparing projects with different time frames, real versus nominal dollars, relative price changes, long-lived projects and calculating horizon values, time-declining discounting, and sensitivity analysis in discounting.

Appendix 6A presents some shortcut formulas for calculating the present value of annuities and perpetuities. The topics covered in this chapter are essentially uncontroversial. Readers who are familiar with the time value of money and capital budgeting techniques may want to skip this chapter.

Cost-benefit analysis often requires us to predict the future. Whether it is desirable to begin a project depends on what we expect will happen after we have begun. But, as mere mortals, we rarely are able to make precise predictions about the future. Indeed, in many situations analysts can be certain that circumstances largely beyond their clients’ control, such as epidemics, floods, bumper crops, or fluctuations in international oil prices, will greatly affect the benefits and costs that would be realized from proposed policies. How can analysts reasonably take account of these uncertainties in CBA?

In this chapter, we consider three topics relevant to uncertainty: expected value as a measure reflecting risks, sensitivity analysis as a way of investigating the robustness of net benefit estimates to different resolutions of uncertainty, and the value of information as a benefit category for CBA and as a guide for allocating analytical effort. Expected values take account of the dependence of benefits and costs on the occurrence of specific contingencies, or “states of the world” to which analysts are able to assign probabilities of occurrence. Sensitivity analysis is a way of acknowledging uncertainty about the values of important parameters in our predictions—it should be a component of almost any CBA. When analysts have opportunities for gaining additional information about costs or benefits, they may be able to value the information by explicitly modeling the uncertainty inherent in their decisions. A particular type of information value, called quasi-option value, is relevant when assessing currently available alternatives that have different implications for learning about the future.

Microeconomic theory provides the basic technical foundations for CBA. This chapter begins with a review of the major concepts of microeconomic theory as they apply to the measurement of social costs and benefits. Most of these concepts should be at least somewhat familiar from your previous exposure to economics. After that we move to welfare economics, which concerns the normative evaluation of markets and of policies. We explain how to use microeconomic theory to assess benefits, costs, and net social benefits in CBA.

For purposes of simplicity, we assume the presence of perfect competition throughout this chapter. Specifically, we assume that there are so many buyers and sellers in the market that no one can individually affect prices, that buyers and sellers can easily enter and exit the market, that the goods sold are homogeneous (i. e., identical), that there is an absence of transaction costs, that information is perfect, and that private costs and benefits are identical to social costs and benefits (i. e., there are no externalities). Chapter 4 considers how to measure benefits and costs when some of these assumptions do not hold; that is, various forms of market failure are present.

It seems only natural to think about the alternative courses of action we face as individuals in terms of their costs and benefits. Is it appropriate to evaluate public policy alternatives in the same way? The CBA of the highway sketched in Chapter 1 identifies some of the practical difficulties analysts typically encounter in measuring costs and benefits. Yet, even if analysts can measure costs and benefits satisfactorily, evaluating alternatives solely in terms of their net benefits may not always be appropriate.

An understanding of the conceptual foundations of CBA provides a basis for determining when CBA can be appropriately used as a decision rule, when it can usefully be part of a broader analysis, and when it should be avoided. The goal of allocative, or Pareto, efficiency provides the conceptual basis for CBA. In this chapter we provide a nontechnical introduction to Pareto efficiency. We then explain its relationship to potential Pareto efficiency, which provides the practical basis for actually doing CBA. Our exploration of the roles of Pareto efficiency and potential Pareto efficiency in CBA provides a basis for distinguishing it from other analytical frameworks. It also provides a basis for understanding the various philosophical objections commonly made against the use of CBA for decision making.

Cost-benefit analysis assesses the efficiency of alternative courses of action. It requires prediction of the impacts of the alternatives and their monetization. The first ten chapters lay out the conceptual foundations for predicting and monetizing. This chapter and the following five chapters provide methods and resources for actually completing these tasks in practice. This chapter provides a brief overview of the various approaches to prediction and monetization. The following five chapters describe these approaches in detail

Keep in mind that good CBA predicts and monetizes all the impacts of the alternative policies being considered. Some impacts, such as the use of real resources to implement policies, can usually be predicted with considerable confidence in fairly straightforward ways and monetized directly with market prices. Other impacts, especially those involving people changing their behaviors, can only be predicted indirectly, often based on statistical inference or perhaps even theory alone, and therefore they may be very uncertain. Furthermore, monetizing often requires analysts to use shadow prices, such as willingness to pay for reductions in mortality risk, that are not directly revealed in markets and thus also uncertain. CBA requires analysts to accept these uncertainties to produce a comprehensive assessment of alternative policies.

Imagine that you completed the first three steps in CBA set out in Chapter 1. You have identified the alternative policies, determined standing, and catalogued the relevant impacts with appropriate units of measure. Next you must predict the impacts of each alternative and monetize them. Much of the effort in a typical CBA goes into these two steps. Unlike most social science research, where one can choose to focus on some impacts, perhaps because data are available to support estimation of past effects as the basis for prediction, you do not have the luxury of focusing on some impacts and ignoring others. You must make predictions of all impacts even in the absence of strong supporting evidence. To do this, you should use policy research, relevant theory, and, when all else fails, learn about the subject and make informed guesses. By all means be self-conscious about your uncertainties and be forthright in presenting them. Because of these uncertainties, Monte Carlo simulation and the other forms of sensitivity analyses illustrated in Chapter 7 are essential components of almost any CBA for conveying the degree of certainty in the ultimate CBA prediction of net benefits.

In conducting CBAs of government policies, there is a natural tendency to list as many effects of the policies as one's imagination permits. For example, an improvement in public transportation in a particular city may increase bus usage and reduce car usage. It may also reduce downtown pollution and congestion. In addition, it may reduce the demand for automobile repairs, parking places, and gasoline.

To assess these effects, one must first determine which occur in primary markets and which occur in secondary markets. Primary markets refer to markets that are directly affected by a policy, while secondary markets are markets that are indirectly affected. The changes in bus usage previously mentioned clearly occur in the primary market for public transportation. The reductions in pollution and congestion also can be thought of as occurring in the primary market for public transportation, though these particular impacts are in the external, or missing, part of that market. Any effect that occurs in a primary market should be accounted for in a CBA. On the other hand, effects on the demand for auto repairs, parking places, and gasoline occur in secondary markets and, as will be seen, often can (and indeed should) be ignored in conducting CBA. This last group of effects is often referred to as secondary, second-round, spillover, side, pecuniary, or indirect effects.

While Chapter 4 examined the benefits and costs of government policies that occur in primary markets, this chapter focuses on policy impacts in secondary markets. As in Chapter 4, we distinguish between efficient and distorted markets. In addition, the chapter takes a brief look at the special implications of secondary market effects for local communities, as the benefits of such effects are often touted by advocates of local infrastructure projects such as sports stadiums and convention centers.

This chapter focuses on estimating the benefits and costs of program interventions by using experimental and quasi-experimental designs. The chapter first describes experimental and quasi-experimental designs, indicating how they are used in estimating the impacts of social programs—for example, health, education, training, employment, housing, and welfare programs. The chapter then describes how these impacts are incorporated into CBA of employment and training programs. The chapter concludes by examining actual CBAs of employment and training programs that were targeted at welfare recipients. The chapter provides numerous illustrations of how concepts developed earlier in this book can be used in actual cost-benefit analyses.

Akey concept for valuing policy impacts is change in social surplus. As discussed in Chapters 3 and 4, changes in social surplus are represented by areas, often as triangles or trapezoids, bounded by supply and demand curves. Measurement of changes in social surplus is relatively straightforward when we know the shapes (functional forms) and positions of the supply and demand curves in the relevant primary market, before and after the policy change. In practice, however, these curves are usually not known. Analysts have to estimate them or find alternative ways to measure benefits and costs. In the previous chapter, we discuss how such information can be gathered from demonstrations. In this chapter, we discuss direct estimation of these curves.

We focus on estimating demand curves because, in practice, analysts are more interested in estimating changes in consumer surplus than estimating changes in producer surplus. There are three main reasons. First, changes in producer surplus are often offset by changes in consumer surplus, and thereby constitute transfers. Second, they are often negligible. Third, for ex post CBA, changes in producer surplus can usually be computed more easily from changes in profits, which can often be obtained directly from affected firms. There are, however, two major situations when it is important to estimate the supply curve. One pertains to unemployed labor, which we discuss in detail in Chapters 4 and 12. The second arises when the government intervenes in otherwise efficient markets for capital, foreign exchange, labor, and other productive assets. We discuss these issues in Chapter 17.

For goods traded in well-functioning markets, we can usually observe the market clearing price. We may also observe the aggregate quantity bought and sold so that we have the point of intersection of the demand and supply curves. The task in this chapter is to estimate changes in social surplus where there is limited information. We consider three situations. First, we suppose that we have only one observation on the demand curve but previous research provides knowledge of its shape (functional form), and either its elasticity or slope. Second, we suppose that we have a few observations (points) on the demand curve. Third, we suppose that we have many observations of prices and quantities from different regions or from different time periods. In the first two situations we can make simple extrapolations.