Monetizing Benefits

Cost-benefit analysis (CBA) compares the positive and negative outcomes of a policy or program by converting them into a common unit: dollars. Because some benefits are not naturally expressed in dollar terms, they must be monetized—that is, converted into a dollar value—to be included in a cost-benefit calculation. This document explains the methods commonly used to monetize benefits.

Intangible benefits

Criminal justice policies and programs can produce tangible benefits that are directly measured in dollar terms (such as a reduction in the number of jail bed-days used) as well as intangible benefits (such as a decreased fear of crime) that do not have an obvious dollar value. When analysts cannot ascertain the value (or price) of a benefit, they use shadow prices, which represent those monetary values. Shadow prices are typically estimated using stated-preference valuation or revealed-preference valuation.

Stated-preference valuation

One way to calculate a shadow price is to ask people hypothetically how much they would pay for a specific benefit or public good. This approach is called stated-preference valuation and often uses contingent-valuation surveys, which ask people to state how much they would be willing to pay for a certain outcome, such as a reduction in crime. (The terms stated preferences, contingent valuation, and willingness to pay are used almost interchangeably.) For example, a nationwide survey in 1999 found that, on average, adults in the United States would be willing to pay more than $200 per year to reduce the threat of gunshot injury by 30 percent. Based on that figure and the number of gunshot injuries in 1998, the researchers calculated a shadow price of $1.2 million per incident of gun violence. Reducing gun injuries by 30 percent was estimated to be worth a societal benefit of $24.5 billion.

Revealed-preference valuation

Another way to calculate shadow prices is to infer the value of an intangible benefit by looking at market transactions or observable behaviors. Economists typically prefer this approach, called revealed-preference valuation, for estimating shadow prices, because it examines empirical data. A justice-related example is determining the amount people would pay to live in a neighborhood with less crime by looking at differences in real estate values between a high-crime neighborhood and a low-crime neighborhood. This type of revealed-preference valuation is known as hedonic valuation. Using the real estate example, you would collect information about the property (such as location, square footage, number of bedrooms, and the house’s physical condition) and neighborhood characteristics (such as perceptions of safety, quality of public schools, and proximity to transportation). Next, you would use regression analysis to disaggregate those components and assign a value to each one. If we assume that buyers weigh all of these factors when purchasing a home, this approach would ultimately help “reveal” the price consumers would be willing to pay to live in a neighborhood they consider safe.

Keep in mind

Shadow prices for the costs of crime can be used to estimate the benefits that result from a reduction in crime. Many CBAs of justice policies incorporate the perspective of crime victims, and a well-developed body of economic literature provides shadow prices for related costs and benefits. CBKB’s Victim Costs tool provides more information about the methods used to calculate these shadow prices and how to include a reduction in victimization as a benefit when conducting a CBA.

Criminal justice policies may also result in benefits for which there are no readily available shadow prices, such as improved mental health or better parenting outcomes.  When time and resources do not allow for the valuation of outcomes using the methods described, it is essential to discuss these impacts qualitatively, to provide a more complete picture, along with the costs and benefits that have been monetized.