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Some issues in the application of benefit–cost analysis (BCA) remain contentious. Although a strong conceptual case can be made for taking account of the marginal excess tax burden (METB) in conducting BCAs, it is usually excluded. Although a strong conceptual case can be made that BCA should not include distributional values, some analysts continue to advocate doing so. We discuss the cases for inclusion of the METB and the exclusion of distributional weights from what we refer to as “core” BCA, which we argue should be preserved as a protocol for assessing allocative efficiency. These issues are topical because a recent article in this journal recommends ignoring the METB on the grounds that desirable distributional effects offset its cost. We challenge the logic of this article and explain why it may encourage inefficient policies.
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 practice in CBA we calculate expected surplus. However, the theoretically correct measure of net benefits is option price (also called certainty equivalent). The difference between expected surplus and option price is option value. It can be thought of as an insurance benefit. Assuming that individuals are risk averse, expected surplus can either underestimate or overestimate option price depending on the sources of risk. For an individual who is risk averse and whose utility function depends only on income, expected surplus will underestimate option price for policies that reduce income risk and overestimate option price for policies that increase income risk. However, the over or underestimation is generally relatively small. Therefore, analysts can use expected surplus without incurring too much bias.
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
When policies impact goods for which markets do not exist, analysts develop shadow prices for the impacts to monetize them. This chapter assess the advantages and disadvantages of several methods commonly used to find shadow prices. The market analogy method involves using information about the market demand for an analogous good to develop a shadow price for a non-market good. The tradeoff method imputes prices to non-market goods based on how people trade changes in its quantity for other things they value such as time. The intermediate good method involves linking changes in the non-market good to changes in observable measures such as labor market earnings. The asset valuation method looks for changes in the values of assets like stocks or housing that capture changes in non-market goods. The hedonic pricing method employs regression analysis to control for multiple factors that affect asset values and heterogeneity in the demand for characteristics of assets. The travel cost method uses differences in travel time as a proxy for price differences for goods not traded in markets. The defensive expenditures method looks at how changes in non-market goods affect the production functions for market goods.
Cost-effectiveness Analysis (CEA) is often used instead of CBA in areas 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. Second, the only cost analysts or decision-makers want to consider is the financial cost of the technology (i.e., the policy alternative) incurred by the government agency that will pay for it, such as the public health plan. Cost-utility analysis also relates costs to a single benefit measure, but the benefit measure is a construct made up of several (usually two) benefit categories, reflecting both quantity and quality. For example, in health technology assessment, the benefit measure is usually quality-adjusted life-years (QALY), which combines both the number of additional years of life (mortality) and the quality of life during those years (morbidity).
The broad purpose of Cost-Benefit Analysis (CBA) is to help social decision making and to increase social value or, more technically, to improve allocative efficiency. CBA is a policy assessment method that quantifies in monetary terms the value of all consequences (usually called impacts) of a policy to all members of society. This chapter provides a non-technical overview of the ten steps involved in performing a CBA. It involves issues around standing, prediction of impacts, monetization of impacts, discounting and sensitivity analysis.
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.
Chapter 19 examines the role of the distribution of benefits and costs among groups in using CBA for decision making. Although those affected by a policy can potentially be divided into groups along many dimensions— income levels, age, gender, race, ethnicity, location, and so forth—the chapter emphasizes CBAs of policies that have differential effects on groups that differ by income—for example, projects that are located in underdeveloped regions or programs targeted at disadvantaged persons. The chapter first examines the economic rationale for treating dollars received or expended by various income groups differently in CBA. It then considers using distributional weights--numbers such as 1, 2, or 1.5 that are intended to reflect the value placed on each dollar paid out or received by different groups--for doing this in practice. Chapter 19 examines how these distritributional weights might be estimated. For illustrative purposes, distributional weights are applied to a number of previous CBAs welfare-to-work programs. The chapter makes clear that a widely agreed upon set of distributional weights do not yet exist and, therefore, distributional weighting is controversial.
This chapter deals with the practical issues one must know in order to compute the net present value of a project. It assumes the social discount rate is given. The chapter covers: present value, future value, and net present value; annuities and perpetuities; continuous compounding; the timing of benefits and costs; comparing projects with different time frames; inflation and the difference between nominal and real dollars; relative price changes; terminal values in fixed-length projects; terminal (horizon) values in long-lived projects; time-declining discount rates; sensitivity analysis in discounting; and the internal rate of return as a decision rule. Appendix 9A provides shortcut formulas for calculating the present value of annuities and perpetuities with and without growth.
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.
Chapter 5 focuses on output markets, the markets in which policy interventions take place. Extensive use of demand and supply curve diagrams in this chapter, and in the next two chapters, to help clarify the discussion. The chapter begins with a brief discussion of shadow pricing. Next, it discusses valuing impacts in efficient markets. followed by a discussion about how to value impacts in distorted markets where market failures are found. The chapter demonstrates how policies to correct five prominent types of market failures—monopoly, information asymmetries, externalities, public goods, and addictive goods—can be analyzed within a CBA framework. One reason for discussing market failures is that their presence provides the prima facie rationale for most, although not all, proposed government interventions that are assessed through CBA.
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.