Generally, estimation of changes in social surplus requires knowledge of entire demand and supply schedules. Chapter 4 discusses direct estimation of demand and supply curves, focusing on the demand curve for the purpose of measuring consumer surplus. It assumes that there is a market demand schedule for the good in question, such as garbage collection or gasoline, and we can observe at least one point on this demand curve. In many applications of CBA, however, the markets for certain “goods,” such as human life or pollution, do not exist or are imperfect for reasons discussed in Chapter 3.

In the Affair of so much Importance to you, wherein you ask my Advice, I cannot for want of sufficient Premises, advise you what to determine, but if you please I will tell you how. When those difficult Cases occur, they are difficult, chiefly because while we have them under Consideration, all the Reasons pro and con are not present to the Mind at the same time; but sometimes one Set present themselves, and at other times another, the first being out of Sight. Hence the various Purposes or Inclinations that alternately prevail, and the Uncertainty that perplexes us.

The prices of most goods and services tend to rise over time, that is, we experience inflation. However, in practice, not all prices (or values) increase at the same rate. Some prices, such as house prices and fuel prices, are often much more volatile than others. For this reason, some countries exclude such volatile items from their basket of goods when computing the CPI. And the prices of some goods and services sometimes go in a different direction to other prices. For example, from December 2010 to December 2016 in the US, the all-items CPI rose about 10 percent; however, the price of houses rose about 21 percent, while the price of gold fell about 15 percent.1

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.

In the actual practice of ex anteCBA in circumstances involving significant risks, analysts almost always apply the Kaldor–Hicks criterion to expected net benefits. They typically estimate changes in social surplus conditional on particular contingencies occurring, and then they compute the expected value over the contingencies as explained in Chapter 11. Economists, however, now generally consider option price, the amount that individuals are willing to pay for policies prior to the realization of contingencies, to be the theoretically correct measure of willingness to pay in circumstances of uncertainty or risk.

Educational attainment can benefit society. Most directly, it usually increases the productivity of the educated. It can also provide external benefits to the rest of society by reducing the risks that individuals commit crimes or become dependent on social services. In developed economies, these external benefits are likely to be largest when attainment involves moving beyond secondary schooling. For example, in the United States only 83 percent of individuals overall, and 75 percent of African Americans, earn high school diplomas.1

Numerous studies have examined the benefits from and costs of higher education. The major benefit considered in these studies is the increase in earnings that results from additional education, some of which accrues to the individual and some of which accrues to the government as taxes. The major costs that are typically considered are school operating costs (which are covered by tuition, tax subsidies, and donations) and earnings forgone by students as a result of enrollment. This leaves out other potential benefits and costs, many of which occur outside the marketplace and some of which may be of considerable importance to students, other members of society, or both. These include, for example, higher education’s effects on the quality of life, child rearing, economic growth, health, crime, and governance.

This case summarizes an ex post CBA of the Tulsa Individual Development Account (IDA) program. The analysis is based on data collected 10 years after the initial random assignment of participants into treatment and control groups and about six years after the program ended.1 Most relevant to the subject of this chapter, it considers the distributional consequences of the IDA program from participant, government, and donor perspectives. In addition, it uses Monte Carlo simulation to assess uncertainty in the IDA CBA.

When evaluating government policies or projects, analysts must decide on the weights to apply to policy impacts that occur in different periods, which are normally measured in years.1 Using weights, denoted by wt, and estimates of the real annual net social benefits, NSBt, the estimated net present value (NPV) of a project is given by:2

Government policies, programs, and projects affect some individuals differently from others. Thus, in conducting CBAs, analysts sometimes report – indeed sometimes are required to report – benefits and costs for separate categories of people. The relevant classification of individuals into groups for this purpose usually depends on the specific policy under evaluation. Some common examples of categories include: consumers versus producers versus taxpayers, program participants versus non-participants, citizens (of a nation or a state or a city) versus non-citizens, and high-income groups versus low-income groups.

Cost–effectiveness analysis (CEA) is widely used as an alternative to CBA, especially in policy arenas such as education, health, and defense. It is used in situations with two characteristics. First, the policies being evaluated have one major benefit that analysts or clients are unwilling or unable to monetize. Many public health programs have this characteristic: their major purpose is to save lives (reduce fatality risk) and it may be possible to predict the numbers of statistical lives saved by alternative public health programs, but decision-makers are unwilling to place a monetary (dollar) value on a life saved. In national defense, analysts sometimes deal with intermediate goods with tenuous linkages to individual preferences.